Sylvain Viroulet

Alban Sauret

O Kimmoun

Tsunami generated by a granular collapse down a rough inclined plane

Keywords: 47, 57, Gc -Granular flow, complex fluids 92, 10, hl -Tsunamis 45, 70, Ht -Avalanches (granular systems)

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Tsunami generated by a granular collapse down a rough inclined plane

In the last decade, two particularly destructive tsunamis, in the Indian Ocean [1] and in Pacific Ocean [2], have raised the necessity to predict, or at least evaluate the hazards induced by such events for the population and human activities. Although many tsunamis arise from underwater earthquakes [3], some are also induced by submarine and subaerial landslides which can be locally more dangerous as they form near the coast and may exhibit extreme runup [4].

One approach to study tsunamis generated by subaerial landslides relies on the simple configuration of a solid block impacting the water [5,6]. During a landslide, strong interactions occur between the slide and the generated waves. In this case, estimating real situations requires a deformable slide [7]. Most previous studies focused on the particular case of a Froude number F r = v s / √ g H larger than one, i.e. on a slide velocity v s larger than the shallow water wave velocity √ gH (g being the gravity, H the water depth). This configuration is relevant to model geophysical events such as the mega-tsunami of Lituya Bay, Alaska (1958) where the estimated Froude number was about F r ≃ 3.2 [8]. However, in the simple situation of a cliff collapse, which is expected to happen in the South of France [9], the dynamic would be different as the Froude number becomes smaller than unity. In this situation, an estimate of the interplay between the granular material and the wave is required. Despite important implications on risk control, landslides for a Froude number smaller than one and the generation of the subsequent tsunamis have been poorly investigated.

The lack of such studies may be explained by the difficulty of modeling dense flows of granular media despite their major stake in industrial and geophysical applications [10,11]. Due to these applications, a huge number of studies considered various dense flows of dry granular materials [12][13][14][15]. The mechanical properties of the grains are not completely understood but recently, Jop et al. [16] suggested a continuum model, the µ(I)-rheology, to account for the unique properties of dry dense granular flows. This rheology gives a good description in many situations such as a granular collapse [17,18] or granular flows on a pile [16]. However, the addition of water drastically changes the dynamics of granular flows as emphasized by recent studies with partially wet granular media [19][20][21][22][23], suspension of particles [24][25][26] and underwater granular flows [27,28]. These studies show the influence of both the capillary effects and the surrounding fluid, but they also emphasized the difficulty in predicting the dynamics of the granular media and their interplay with the fluid. In particular, underwater granular events are complicated to describe due to the contradictory effects of the drag force and the lubrication of the grains [29]. It becomes even more complex to describe the behavior of the impact of dry granular media from the air into water and the subsequent generated waves.

In this Letter, we rely on extensive experiments to analyze the interactions between the granular flow and the waves generated by the landslide at Froude numbers smaller than one. Typically, in our experiments F r ∼ 0.2 -0.8. The present situation is relevant to model tsunamis generated by cliff failures located just above the sea surface, which are characterized by low impact velocities.

The experimental setup is sketched in Fig. 1(a). It consists of a glass tank of dimensions 2.20 m long, 0.40 m high and 0.195 m wide. The water depth considered in this study is H = 15 cm. Three different granular materials are used: spherical glass beads of diameters 4 and 10 mm and density ρ GB ≃ 2500 kg.m -3 , and a non-spherical aquarium sand of mean diameter 4 ± 1 mm and density ρ AS ≃ 2300 kg.m -3 . The slope is made of a polyvinyl plate roughened by gluing one layer of the considered granular media onto it to ensure no-slip boundary conditions at the bottom. A vertical gate, opened vertically, delimits the initial reservoir of granular materials located just above the undisturbed free surface. The generation of the wave and the evolution of the granular flow are recorded using a high speed camera at 100 fps (Phantom V641, 4M Pixels). The shape of the granular material is highlighted using a laser sheet located above the free surface. The evolution of the free surface elevation is measured using four resistive wave gauges with a sampling frequency of 200 Hz, which is sufficient to capture the dynamics of the waves. The gauges are located at 0.45 m, 0.75 m, 1.05 m and 1.35 m from the lifting gate.

Typical experiments are illustrated by successive snapshots of the granular collapse in a dry case (Fig. 1(b)-(e)) and in presence of water (Fig. 1(f)-(i)) for an initial mass m = 2 kg of aquarium sand and a slope θ = 50˚. It shows that the presence of water modifies the shape and the timescale of the granular front during the collapse, which occurs in an inertial regime [27,28,30]. Also note that the time scale of the granular collapse in air is smaller than in the presence of water and that their morphologies are different (see Fig. 1(c) and 1(g) taken at the same instant). In water, the surrounding fluid generates an important drag on the granular material which leads to the generation of a larger front. At the same time, a wave is generated at the surface on a timescale similar to the granular collapse, and thus it is not possible to decouple the two different phenomena.

Let us consider the propagation of the leading wave, which is also the largest one, and is followed by an oscillatory wave train. We assume that the initial amplitude of the leading wave is known and we focus here on its propagation. We shall see in the following how the slope influences the amplitude of the leading wave. We ensured that the experiments are completely reproducible. We report in figure 2 the evolution of the maximum amplitude of the generated leading wave as a function of the distance to the collapse for various slope angles, initial mass and different granular materials. For each experiment, we rescale the amplitude at the probes by the amplitude A 1 at the first probe. To explain the dependence of the tsunami amplitude with the distance from the source, x, we consider the weakly dispersive linear shallow water approximation. In this approximation, the free surface elevation η is described by the the linearized Korteweg-de-Vries equation [START_REF] Whitham | Linear and nonlinear waves[END_REF]:

∂η ∂t + c ∂η ∂x + c H 2 6 ∂ 3 η ∂x 3 = 0. ( 1
)
10 0 10 1 0.4 0.6 0.8 1 1.2 x p /H A/A 1 0 0.5 1 1.5 2 -2 -1 0 1 2 t (s) η (cm) 0 0.5 1 1.5 2 -2 -1 0 1 2 t (s) η (cm)
Fig. 2: Evolution of the dimensionless amplitude of the generated leading wave in logarithmic scale for various masses and various slopes using glass beads of 4 mm (•), 10 mm ( ) and aquarium sand (⋄). The dashed line is the scaling law x -1/3 . The uncertainties are plotted on the blue circles. Inset: evolution of the wave amplitude η at the first and third probes for m = 2 kg of 4 mm glass beads and a slope of 45˚.

A solution to this equation is

η(x, t) = Q 2 c t H 2 1/3 Ai 2 c t H 2 1/3 (x -c t) , (2)
where Q is a constant corresponding to the cross-section of the water volume displacement, H is the water depth, c = √ gH and Ai is the Airy function. Thus, during the propagation, the amplitude of the leading wave decays as t -1/3 , which leads to a dependence as x -1/3 . This scaling law agrees with the experimental data regardless of the initial mass or slope (see Fig. 2). Note also that the viscosity and the interfacial tension have negligible effects on the propagation of the wave as observed for solid body impact [START_REF] Viroulet | [END_REF] or granular impact at F r > 1 [7,33]. Thus, using a far-field approximation, we can estimate the amplitude of the waves during its propagation. We now need to estimate the influence of the different parameters, such as the slope angle or the mass of granular media, on the initial amplitude of the wave. In the following, we focus on the influence of the granular flow properties on the wave generation. The evolution of the run-out distance of the granular flow with the slope angle θ for both dry collapse and in presence of water is shown on Fig. 3. In our experiments, the aspect ratio of the slide is related to the slope angle by h/L = tan θ, where h is the initial height of the slide and L is the initial length. Previous studies have considered the run-out distance on a slope [34,35], but only in a dry case. In our situation, we clearly see that the data is separated into two main curves. The run-out distance associated to the collapse of glass beads without water, as well as the underwater collapses, are all located on the upper curve. It suggests that for spherical materials, the runout remains the same if the collapse occurs in one phase only (air or water). Indeed, in our case the viscous effect is negligible compared to the inertia of the particles. However, we also notice that the runout distances associated with the collapse of glass beads from air into water collect in the lower curve. This is likely due to the presence of air captured during the collapse which decreases the velocity of the slide. Also, the interaction between the generated wave and the granular collapse can have a non-negligible contribution. Note that the aquarium sand has a peculiar behavior as the run-out distance is the same in all the situations, which may be due to the coarse shape of the aquarium sand that limits the velocity of the particles. The runout distance follows a power law R stop = λ(θθ c ) α with λ = 0.32, α = 0.56 and λ = 0.26, α = 0.51 for dry and into water collapse respectively. The critical angle θ c is the angle of avalanche measured experimentally [36]. The value of the critical angle for each granular material is represented in table 1. The contradictory effects of the ambient fluid observed by Topin et al. [29] for an underwater collapse which can enhance the granular flow are not observed in the configuration of dry impact into water in the inertial regime. Note that in our experimental setup, dry granular collapse and underwater collapse leads to a similar runout distance. But when the granular material impacts the free-surface of the water, the runout is smaller. Indeed, a larger amount of energy is dissipated by internal friction because of the presence of air captured in the granular material. As observed by Topin et al. [29], the added hydrodynamic forces of the ambient fluid on the granular material increase and so the duration of the collapse is larger for impact into water. Note also that the difference between wet and dry collapse is likely due to the speed at which the granular materials spread over the bottom. It also explains why the aquarium sand have a peculiar behavior in our experiments. Its runout is the same in both the dry case and in presence of water because the velocity of the granular front remains much lower for aquarium sand than for spherical glass beads. It seems to indicate that for the aquarium sand, the friction on the bottom and between the different layers prevails over the hydrodynamic forces of the ambient fluid. granular material diameter (mm) θ c small glass beads 1.5 25, 7 The amount of rocks collapsing into the water is a relevant parameter for geophysical events and is thus also considered experimentally. Fig. 4 reports the influence of the initial mass of granular material on the wave amplitude. The slope angle remains constant at 45 • and the three different granular materials are considered. From the previous results we can restrict our study to the first wave gauge since the later propagation of the wave can be estimated by the shallow-water model. In all of the experiments, the volume fraction of the granular media is estimated to be 0.60 ± 0.05. Note that this value is estimated to be slightly larger for the aquarium sand because of the non-sphericity of the particle and the more important polydispersity. However, the larger compaction of the aquarium sand associated with its lower density leads to an equivalent density of the granular slide ρ eq = φρ g for the three different granular materials, where φ is the volume fraction and ρ g is the density of the granular material. The amplitude of the wave at the first probe increases with the initial mass following a power law A 1 ∝ m α with α ≃ 0.9 ± 0.05 for the three granular media. Note that predicting the exact dependence of the wave amplitude on the mass of the collapse remains a challenging issue for waves generated at low impact velocity. Indeed, in previous studies [7,37], the Froude number at the impact was always larger than unity such that all the initial amount of granular material contributed to the generation of the leading wave. However, in the present study, when the leading wave starts propagating part of the granular material is still collapsing along the slope for large mass of the slide. Therefore, all the initial mass does not generate the first wave and future experiments will have to consider the time scale of the collapse compared to the time scale of the wave generation. Note also that in our configuration, we were only able to use glass beads of the same density. We are, thus, not able to tune the mass of the material without modifying the volume accordingly. Further experiments using beads of different density would be of interest to investigate the relative influence of the initial mass and the initial volume of the slide.

In previous studies, the velocity of the slide was large enough to neglect the influence of the slope and the deformation of the granular media [7,37]. Here, the velocity of the granular materials at the impact is close to zero and the interplay between the fluid and the beads is important. Therefore, the previously obtained results cannot be used to estimate the amplitude of the leading wave generated by cliff failure. We have reported in Fig. 5 the influence of the slope on the maximum amplitude at the first wave gauge for an initial mass of 2 kg. For a given granular material, the amplitude increases with the slope. In addition, our experimental results suggest that the relevant parameter to describe the influence of the slope is the difference between the slope and the critical angle of the considered granular media. Using this new variable, the results collapse quite well for the different granular media. It also shows a saturation at large slope, θθ c > 30 • , where the amplitude of the leading wave reaches a maximum. However, even if the amplitude of the wave saturates, increasing the slope still transfers energy to the waves as illustrated in figure 5, where we have evaluated the evolution of the energy contained in the wave train for all the granular materials. The energy per unit width is estimated by the relation E = 2 E p = ρgc η 2 dt where η is the freesurface elevation. The results collapse onto a master curve and a transition is visible around θ-θ c ≃ 10˚. Note that a given amplitude for the first wave does not necessary lead to the same wave train and thus to the same total energy associated with the tsunami. Indeed the behavior of the collapse is very different between each granular material for the same θθ c so the generated wave train will be different. 50% to nearly 80% of the energy of the wave train is located in the first wave, varying with the granular material and the slope angle. For large slope angle, the amplitude of the first wave does not increase anymore and the remaining part of the energy is transferred in the following waves. An example is provided in figure 6 where the amplitude of the second wave increases with the slope angle whereas the amplitude of the first wave remains the same.

Our experiments illustrate that the total energy associated to the generated wave train does not depend on the granular media, but only on the parameter θθ c . Understanding the exact dependance of the energy of each waves with the slope angle or the initial mass of granular material remains complicated. However, a first approach consists in evaluating the ratio of the energy of the generated wave train and the variation of the mechanical energy of the slide. Initially, the slide is at rest above the free surface, such that the total amount of energy transferred to the system is equivalent to the variation of its potential energy ∆E m = E p (t = 0) -E p (t f ), where: In these expressions, m is the mass of the slide, H denotes the water depth, θ is the slope, L represents the width of the tank, A and z G are the area and the vertical position of the center of mass of the slide at the end of the collapse, respectively, φ i , φ f are the volumic fraction of the slide at the beginning and at the end of the collapse, respectively and ρ w , ρ g are the density of water and the granular material, respectively. The volumic fractions of the slide at the beginning and at the end of the collapse, φ i and φ f , can be expressed by :

E p (t = 0) = m g H + 2 3 2 m tanθ φ i ρ g L , (3)
E p (t f ) = (m -φ f ρ w A L) g z G . (4)
φ i = m ρ g A(t = 0) L (5)
φ f = m ρ g A(t f ) L (6)
These expressions show that the ratio between the energy of the wave train and the total available energy in the system never exceeds 15% regardless of the slope angle or the initial mass of the slide. The highest values occurred for high slope angle because the speed of the slide increases with the slope such that less energy is damped by the friction on the slope. Note, that these values are lower than those observed by Fritz et al. [37] for impact at high Froude numbers, which confirms that taking into account the roughness of the slope drastically reduces the amount of energy transferred into the generated wave train. Understanding the quantity of energy lost by friction and how it is transferred in the several waves remains a challenging issue.

To conclude, we illustrate in this Letter the complex dynamic associated to the collapse of dry granular media into water with the objective of predicting the amplitude of the tsunami generated by a major cliff collapse. Our results uncovered some important findings such as the evolu-tion of the wave during its propagation and the difference between dry and wet granular collapse. We have characterized the influence of the mass and we showed that the effect of the slope can be determined by rescaling the data with the variable θ-θ c . However, the influence of water on dense flows of granular media remains an active problem and characterizing the interplay between the fluid and the slide during its collapse is a key issue to accurately predict the size of the generated waves. Examining the particular cases of collapse into deep water and collapse into a thin layer of water will be one of the aims of future research as these two extreme cases may simplify the situation. In addition, these experiments can be used as benchmark for future DEM-CFD simulations using a two-phase flow which would provide an insight into the dynamic of the granular beads [29].

Fig. 1 :

 1 Fig. 1: Schematic view of the experimental setup. The shape of the granular flow and the generation of the waves are recorded by the high speed camera. The evolution of the amplitude is measured with four contact-type wave gauges. Successive instants of the collapse of the granular material (m = 2 kg) (b-e) in the dry case, and (f-i) in presence of water. The pictures are taken every 0.2 seconds and the scale bar is 5 cm.

Fig. 3 :

 3 Fig. 3: Dimensionless runout distance as a function of the slope angle for the three different granular media: glass beads 10 mm ( ), 4 mm (•) and aquarium sand (⋄). Full symbols are the results for a dry granular collapse and empty symbols are in presence of water. The red asteriks are underwater collapse using 4 mm glass beads. The two dashed line are the best fits for the two regimes. Inset: schematic of the situation.

Fig. 4 :

 4 Fig. 4: Evolution of the amplitude at the first probe for a slope angle of 45 • , as a function of the initial mass for the 10 mm glass beads ( ), 4 mm (•) and aquarium sand (⋄). The error bars are illustrated on the data for 4 mm glass beads and the aquarium sand. The dashed-line is the best fitting slope m 0.9 .

Fig. 5 :

 5 Fig.5: Evolution of the energy of the generated wave train as a function of the slope angle for an initial mass of 2 kg. Inset: evolution of the amplitude at the first wave gauge as a function of the slope angle for an initial mass of 2 kg. In both figures (•) are for 4 mm glass beads, ( ) for 10 mm and (⋄) for the aquarium sand.

Fig. 6 :

 6 Fig.6: Evolution of the free surface at the first probe as a function of the slope angle with the glass beads of 4 mm, at an angle of 50 • (continuous line), 55 • (dash-dotted line) and 60 • (dashed line) .

Table 1 :

 1 Mean particle diameter, d and critical angle of avalanche θc for the four different granular media.

	medium glass beads large glass beads aquarium sand	4 10 ≃ 4	23, 3 • ± 0, 8 • 20, 1 • ± 1, 2 • 37, 3 • ± 0, 6 •

• ± 0, 9 •

Acknowledgements. -We thank Olivier Pouliquen, Pascale Aussillous and Pierre-Yves Lagrée for fruitful discussions.


Thomas Stütz
email: thomas.stuetz@fh-salzburg.ac.at

Florent Autrusseau
email: florent.autrusseau@univ-nantes.fr

Andreas Uhl
email: uhl@cosy.sbg.ac.at

Non-Blind Structure-Preserving Substitution Watermarking of H.264/CAVLC Inter-Frames

In this work we propose a novel non-blind H.264/CAVLC structure-preserving substitution watermarking algorithm. The proposed watermarking algorithm enables extremely efficient watermark embedding by simple bit substitutions (substitution watermarking). The bit-substitutions change the motion vector differences of non-reference frames. Furthermore our watermarking algorithm can be applied in applications scenarios which require that watermarking preserves the length of the bitstream units (structure-preserving watermarking). The watermark detection works in the image domain and thus is robust to video format changes. The quality and robustness of the approach are in depth evaluated and analyzed, the quality evaluation is backed up by subjective evaluations. Comparison to the state-of-the-art indicates a superior performance of our watermarking algorithm.

I. INTRODUCTION

H.264 watermarking has been researched intensely 1 and is of great interest due to its wide applicability in the context of DRM (digital rights management). This paper presents a novel H.264 CAVLC watermarking technique that allows to implement watermarking by simple and efficient bit substitutions of the compressed bitstream (substitution watermarking). Additionally our algorithm is structure preserving, i.e., precisely preserves the length of the bitstream and even of the bitstream's smaller units. In the case of H.264, structure preserving watermarking denotes watermarking algorithms in which the network-abstraction layer units (NAL units / NALUs are small units which form the entire H.264 bitstream) have exactly the same length in the watermarked content and the original content. The structure preservation for H.264 is required for the watermarking of Blu-Ray content. The length preservation is required as the video has to fit on a Blu-Ray disc. The internal structure has to be preserved as often bytebased addressing schemes are employed in production and presentation, e.g., the meta-data on Blu-Ray discs employs byte-based addressing schemes. Blu-Ray players can enhance the presentation with additional online content, which employs byte-based addressing (BD-J) as well.

T. Stütz is with the Department of Multimedia Technology, University of Applied Sciences Salzburg, 5412 Puch/Salzburg, Austria, e-mail: thomas.stuetz@fh-salzburg.ac.at A. Uhl is with the Department of Computer Sciences, University of Salzburg, 5020 Salzburg, Austria, e-mail: uhl@cosy.sbg.ac.at F. Autrusseau is with the LUNAM Université, Université de Nantes, IRCCyN UMR CNRS 6597, Polytech Nantes, rue Christian Pauc, BP 50609, 44306 Nantes, France Special thanks to SONY DADC, in particular Reinhard Blaukovitsch, for the cooperation in the project and the insights into industry requirements. This work has been partially supported by the FFG bridge project 834165. 1 ACM digital library reports 180 publications on H.264 watermarking. IEEE Xplore reports 72 publications on H.264 watermarking.

Another important application scenario of our approach is the online distribution of video content, which benefits from efficient adaptation of the content to the user's device requirements and to the user's current bandwidth. H.264 enables the efficient adaptation of content based on simple bitstream operation, e.g., the aspect ratio can be changed by dropping parts of the bitstream. The scalable extension of H.264/CAVLC offers even more adaptation possibilities (bitrate, resolution, frame rate, quality), which are also implemented with simple bitstream operations. As meta-formats for adaptation employ byte-addressing, structure-preserving watermarking works well together with such adaptation operations, as the byte-addressing remains unchanged.

So there are important applications, which require structurepreserving H.264 watermarking. In our proposed watermarking algorithm the embedding stage is split into an analysis and a substitution stage; analysis must only be conducted once, afterwards the embedding of different marks requires only extremely light-weight bit substitutions. Thus the embedding of numerous marks in real-time with very low computational complexity is possible; of utmost importance for streaming individually marked content to numerous clients. Our main contribution is the proposal of a new non-blind structure preserving H.264 CAVLC watermarking approach. A further contribution is the thorough analysis of the approach with respect to robustness and quality. The quality evaluation not only employs state-of-the-art quality assessment tools, but also presents an actual subjective quality evaluation. Our evaluations focus on 720p content (the leading mobile phone's resolution and also occasionally employed for Blu-Ray content). A brief review and comparison to the state-of-the art of H.264 watermarking with a focus on structure-preserving watermarking is presented in section II. In section III an overview of H.264 is given, while section IV briefly summarizes quality evaluation of visual data. Our structure-preserving H.264 CAVLC watermarking approach is presented in section V. Experimental results with respect to quality and robustness are presented in section VI. Finally section VII concludes the paper.

II. PREVIOUS WORK

There is a considerable amount of scientific literature on structure-preserving watermarking [START_REF] Chung | Digital watermarking for copyright protection of MPEG2 compressed video[END_REF][2] [START_REF] Bijan | Watermarking of MPEG-2 video in compressed domain using VLC mapping[END_REF] for the MPEG-2 format, which is a simple video compression system compared to H.264. For MPEG-2 structure-preserving watermarking is easier as compared to H.264, because MPEG-2 employs less prediction and content-adaptive coding. Therefore MPEG-2 offers more syntax elements which can be simply replaced in the bitstream and employed for structure preserving watermarking. Probably due to the difficulty of implementing structurepreserving watermarking for H.264 the corresponding literature is far less extensive: only one approach can be employed for structure-preserving H.264 CAVLC watermarking [START_REF] Zou | H.264/AVC substitution watermarking: a CAVLC example[END_REF] and only one for structure-preserving CABAC watermarking [START_REF] Zou | H.264 stream replacement watermarking with CABAC encoding[END_REF]. However, there is a considerable amount of literature for H.264 watermarking. Many schemes exploit the H.264 encoding process to embed the watermark during compression. The main advantage of such approaches [START_REF] Noorkami | A framework for robust watermarking of H.264 encoded video with controllable detection performance[END_REF] is that the error introduced by watermarking is not propagated further (at the cost of some bitrate increase). Other schemes work on the bitstream, mostly to reduce the computational burden of compressionintegrated watermarking schemes. It has to be noted that H.264 bitstream watermarking actually performs entropy-decoding, such that the syntax elements can be accessed, watermarked (e.g., the quantized DCT coefficients), and again entropyencoded. The approaches presented in [START_REF] Noorkami | Compressed-domain video watermarking for H.264[END_REF] are examples for H.264 bitstream watermarking. Most related to our application requirements is the setup in the work of Zou and Bloom [START_REF] Zou | H.264/AVC stream replacement technique for video watermarking[END_REF], [START_REF] Zou | H.264/AVC substitution watermarking: a CAVLC example[END_REF], that discusses substitution watermarking for intra frames of H.264 CAVLC bitstreams, but is not capable to watermark inter coded frames (the vast majority of frames is commonly coded as inter frames, some encoders use intra frames only once at the start of a sequence). Thus methods for substitution watermarking of inter frames are needed. The approach of Zou and Bloom [START_REF] Zou | H.264/AVC substitution watermarking: a CAVLC example[END_REF] modifies the intra-prediction modes which can be implemented by bit-substitutions of H.264 CAVLC bitstreams. Suitable substitutions have to be found in a complex analysis stage, which has to consider intra and inter drift, while our algorithm offers a lightweight analysis stage. Furthermore the marking space, i.e., the number of watermarkable blocks, of our approach is larger than the marking space of [START_REF] Zou | H.264/AVC stream replacement technique for video watermarking[END_REF], [START_REF] Zou | H.264/AVC substitution watermarking: a CAVLC example[END_REF], [START_REF] Zou | H.264 stream replacement watermarking with CABAC encoding[END_REF] (see section VI-D) and thus fewer frames are needed for watermark embedding. Alternatively the larger marking space can be employed to improve the robustness of our approach (a lower detection threshold can be selected for the same probability of alarm). A substitution watermarking algorithm for CABAC, based on motion vector data changes, was presented by the same authors in [START_REF] Zou | H.264 stream replacement watermarking with CABAC encoding[END_REF]. However, CABAC and CAVLC are entirely different, and thus the applicable changes are different. Motion vector data are encoded contextadaptively in CABAC, and thus a computationally complex analysis stage is required in the approach of [START_REF] Zou | H.264 stream replacement watermarking with CABAC encoding[END_REF], while our CAVLC approach is extremely lightweight in comparison. Additionally the number of candidate changes is smaller by an order of magnitude and thus the CAVLC algorithm performs better in terms of a larger marking space, which reduces the number of watermarked frames or leads to an improved robustness (see section VI-D).

III. OVERVIEW OF H.264

The design of H.264 follows the classic hybrid video coding approach [START_REF] Richardson | 264 and MPEG-4 video compression: video coding for next generation multimedia[END_REF]. The frames are processed in 16x16 macroblocks. Each macroblock can be predicted using previously processed macroblocks of the same frame (intra-prediction) or other frames (inter-prediction). The macroblocks can be further subdivided (sub-macroblock partitions), the smallest block size is 4x4. A coded video sequence always starts with the coded data of an intra-predicted frame (I frame). The distortion of I frames spreads on all subsequently decoded frames due to inter prediction. After an I frame inter-predicted frames that may use one reference frame (P frame) or two reference frames (B frame) follow. Frames (even P and B frames) may be used as reference, frames which are not used for inter-frame prediction are called non-reference frames. Inter-prediction is conducted by motion estimation and motion compensation, which are conducted with quarter pixel accuracy. The motion vectors (MVs) of a block are predicted by neighbouring blocks (a detailed description can be found in [START_REF]Advanced video coding for generic audiovisual services[END_REF]) and the motion vector difference (MVD) is actually coded in the bitstream (which codes quarter pixel differences). There are two distinct coding modes in H.264, namely CAVLC and CABAC. CAVLC is computationally less expensive (at the cost of a lower compression performance) and thus is employed in cases where computational complexity constraints outweigh the compression performance. Typical applications are in the context of mobile devices (720p has become the resolution of the lead devices), where power and computational constraints outweigh compression. Furthermore 720p content on Blu-Ray discs can be coded with H.264/CAVLC: there is no need for higher compression as 720p typically fits on a Blu-Ray anyway. In H.264/CAVLC MVDs are not coded context-adaptively, but with variable-length signed exponential Golomb codes. Table I shows the coding of MVD values, each MVD (last column) is coded by an exponential Golomb code (in the column labelled "Codeword"). A separate MVD is coded for the x-and ydirection.

IV. QUALITY ASSESSMENT

As briefly mentioned in the introduction, this work mainly focuses on evaluating the robustness and quality performances of an H.264 CAVLC watermarking operating within the compressed bitstream. In this section, we present both subjective and objective quality assessment of watermarked contents.

A. Subjective Quality Assessment

Ultimately, the quality of the marked content will be judged by human observers, and thus, running a subjective experiment is the best way to evaluate the impact of the watermark on the quality of the protected image/video. During subjective tests, quality (or annoyance) scores are collected from human observers within a controlled environment. Subjective experiments have been of high interest for many decades among the scientific community. Early experiments were conducted to determine an optimal viewing distance on television monitors [START_REF] Jesty | The relation between picture size, viewing distance and picture quality[END_REF], or the detection threshold of a simple spot on a CRT screen [START_REF] Baker | Correlation of spot characteristics with perceived image quality[END_REF], and led the researchers to do an attempt to estimate the subjective quality [START_REF] Peter | Evaluation of subjective image quality with the square-root integral method[END_REF]. Evidently, subjective experiments had to be standardized, in order for other researchers to be able to reproduce and/or compare the results. Thus, the International Telecommunication Union (ITU) has published various reports and recommendations for conducting subjective experiments. Both the Radiocommunication (ITU-R) and Telecommunication (ITU-T) sectors of the ITU are regularly issuing some recommendations for quality assessment (both objective and subjective) of digital images and videos. Two recommendations of particular interest are [START_REF]Subjective video quality assessment methods for multimedia applications[END_REF] and [START_REF]Methodology for the subjective assessment of the quality of television pictures question itu-r 211/11[END_REF]. The recommendation [START_REF]Methodology for the subjective assessment of the quality of television pictures question itu-r 211/11[END_REF] notably specifies the viewing conditions, monitor settings (resolution, contrast), the importance of anchoring is highlighted. It is for instance advised to use at least 15 non expert observers. Some advices are given on the duration of the experiment, and on the possible protocols to use. Among the most commonly used protocols, we can cite the "Double-Stimulus Impairment Scale" (DSIS) and the "Double-Stimulus Continuous Quality Scale" (DSCQs). In [START_REF]Subjective video quality assessment methods for multimedia applications[END_REF], alternative protocols are suggested, the "Absolute Category Rating" (ACR) or the "Pair Comparison" methods are amongst the most common methods. Commonly, the outcome of a subjective experiment is to collect the mean opinion scores (MOS) from the observers for the given input subjective dataset. The MOS are simply computed by averaging the collected scores of all observers for a given content. The alternative to subjective experiments is to utilize objective quality metrics (OQM), which are methods whose goal is to predict the perceived quality. In the upcoming sub-section, we will review the most common types of OQMs.

B. Objective Quality Assessment

Objective quality metrics are mainly of two types. On one hand, statistical quality metrics are very widely used, PSNR, RMSE, or SSIM belong to this category. On the other hand, advanced HVS-based OQMs (such as VIF [START_REF] Sheikh | Image information and visual quality[END_REF], VSNR [START_REF] Chandler | Vsnr: A wavelet-based visual signal-to-noise ratio for natural images[END_REF], CPA [START_REF] Pankajakshan | A multi-purpose objective quality metric for image watermarking[END_REF] or C4 [START_REF] Carnec | Objective quality assessment of color images based on a generic perceptual reduced reference[END_REF]) exploit some properties of the human visual system (such as contrast sensitivity, contrast masking, or luminance adaptation) to provide a prediction of the MOS (predicted mean opinion scores are commonly referred to as MOSp). Thus, once the subjective scores are collected (MOS are gathered), and the MOSp computed for a given set of metrics, a metric performance evaluation is performed. The Video Quality Experts Group (VQEG) issued a report in 2008 [START_REF] Vqeg Mm | Final report from the video quality experts group on the validation of objective models of multimedia quality assessment, phase I[END_REF] providing an analysis of various assessment methods as well as several tools that can be used to evaluate the performances of objective quality metrics. Statistical or advanced HVS metrics could be either full reference (FR) or reduced reference (RR) or even no reference (NR). For a FR metric, the original content is needed as an input, along with the distorted content which needs to be assessed. A RR metric needs the content to be assessed along with a reduced set of features from the original content to compute the MOSp. Finally, NR metrics only need the distorted content as an input in order to provide a prediction. Usually, FR metrics exhibit better performances at predicting the MOS. In the following, only FR metrics are used.

V. A N OVEL INTER FRAME SUBSTITUTION WATERMARKING ALGORITHM

Our proposal for a novel H.264 CAVLC watermarking algorithms takes advantage of MVD modifications. Our watermarking algorithm modifies original MVDs such that the MVD of the watermarked content has equal length as the original MVD and thus the length of the NAL units is preserved as well.

The developed watermarking algorithm is robust, non-blind (additional information is required for detection), and zero-bit (only the presence of a watermark will be detected) [START_REF] Cox | Digital Watermarking and Steganography[END_REF]. The watermarking process is easily reversible, i.e., the watermark can be losslessly removed by a simple substitution of the original bit-sequences. In this paper we only present results for watermarking non-reference inter frames, which do not cause any inter frame drift and thus enable very efficient distortion assessment.

A. Embedding

The watermark embedding aims to alter a distinctive feature (in the current implementation average luminance) of applicable macroblocks. The macroblock's average luminance is changed by altering its MVD. In the current implementation only macroblocks of type P16x16 are analyzed (the majority of the macorblocks of B frames are commonly of this type). Furthermore the MVD change modifies an entire 16x16 block of image data, which enables robust detection. The embedding consists of two stages: an analysis stage and a substitution stage (see fig. 1). Input to the embedding are the H.264 bitstream to be watermarked and the watermarking parameters, such as the key for random watermarking bit generation and a quality control parameter MbDist (macroblock distortion), which is used to control the embedding distortion. Only macroblock MVD changes are considered for watermarking which result in a distortion less than MbDist (in this work we use the mean squared error for distortion estimation). The distortion is computed between the original macroblock and the MVD changed and reconstructed macroblock. Output of the process are the watermarked bitstream and little additional information for detection ("Detection Info" in fig. 1). In the analysis stage each macroblock is checked for watermarking suitability, thereby several conditions have to be met such that a macroblock is employed for watermarking. Only inter predicted macroblocks are considered for watermarking, and each length-preserving and sign-preserving MVD change is I) and same length are evaluated. First the quality is checked (after application of the change) which is done by computing the MSE (mean squared error) between the original macroblock (with the original MVD) and the modified macroblock. Only if the obtained MSE is below the quality control parameter MbDist the change is considered valid. With the parameter MbDist the embedding strength can be adjusted. Second the impact on the feature (avg. luminance) is computed, which is later used in the detection process. If a macroblock has at least one MVD change that increases the feature and at least one MVD change that decreases the feature, the macroblock is suitable for watermarking. The change with the strongest increase is employed to encode a 1, while the change with the strongest decrease is employed to encode a -1. The macroblock position and frame number and the original feature are recorded in detection info. The watermark embedding algorithm is summarized briefly as follows.

For each inter-predicted macroblock:

• Evaluate original block's feature (avg. luminance)

• Apply length-and-sign-preserving MVD change and check -Embedding distortion is below MbDist (MSE).

-Feature difference is sufficient (avg. luminance larger then, e.g., 0.25)

• If there are two groups of changes (increase feature, decrease feature) use the 'decrease feature' change to encode a minus one, and the 'increase feature' change to encode a one.

B. Detection

The detection can be performed in the image domain and does not require any re-encoding. As the presented approach is non-blind, we can assume perfect registration / alignment (temporal and spatial). The actual implementation of registration is well covered in computer vision literature and can for example be solved by storing SIFT interest points [START_REF] David | Distinctive image features from scale-invariant keypoints[END_REF] of the watermarked frames as well as the detection information. These features can later be used to register the content. Specific solutions for watermarking have also been proposed [START_REF] Cheng | Spatial temporal and histogram video registration for digital watermark detection[END_REF], [START_REF] Chupeau | Temporal video registration for watermark detection[END_REF].

The detection process can be divided into three distinct tasks, bit extraction, correlation and decision (see figure 2). The overall watermarking system including detection is illustrated in figure 3. The bit extraction takes advantage of the detection info, it computes the feature (avg. luminance) of a possibly watermarked macroblock and compares it to the Fig. 2. Watermark detection recorded original feature. If the computed feature is larger or equal, a 1 is extracted; if it is smaller a -1 is extracted. In the correlation step, the extracted bit sequence ẽ is compared to the possibly embedded watermark bit sequence ~w . The number of embedded bits is n. More precisely, the detector response z is computed by:

z =1/n n X i=1 e i × w i
Finally depending on the detector response and a user-defined false positive probability a decision is made. Thereby the detector response is compared to a detection threshold T (p fp ), and if the detector response is larger than T the watermark is decided to be present. The user-defined false positive probability determines how likely it is to detect the watermark in content that does not contain the watermark. Overall the algorithm for detection can be briefly summarized as follows:

For each possibly watermarked macroblock in the image domain:

• Compute the block feature (avg. luminance) of the possibly watermarked macroblock and compare the block feature to the original feature and return -1 for a decrease and 1 for an increase.

• Compute detection statistic (a measure of correlation between embedded and extracted sequence).

• Decide whether watermark is present or not. This kind of watermark detection is referred to as detection based on hard decision decoding [25, sect. 2.4.2.1] and is well known and has already been analysed.

The analysis of the distribution of the detector response can be divided into two cases, the watermark has not been embedded (H 0 ) and the watermark has been embedded (H 1 ).

After embedding and without any distortions (e.g. recompression) the detector response will always be 1, as we do not have any inaccuracies / randomness in the embedding and detection processes and thus each bit will be correctly extracted. Distortions from recompression and other signal processing operations will introduce errors and shift the detector response for watermarked content slightly towards zero.

In case of H 0 the distribution of the detector response follows a Binomial distribution, which can be approximated by a Gaussian distribution.

Figure 4 gives an overview how the number of embedded bits and the false positive probability determine the appropriate threshold for detection. The more bits are embedded the lower the threshold for a given false positive probability can be chosen.

For our application scenarios, the exact determination of the false positive probability is of ultimate importance. In case of of a false positive in the online distribution scenario, i.e., Fig. 3. Watermarking overview Fig. 4. The threshold for various probabilities of false alarm as a function of the number of embedded bits a watermark is detected in pirated content, a user is found guilty of distributing the content and certain actions (legal or technical ) are taken, which should prevent the user from further piracy. Given that the user is innocent the rage and the following (social) media coverage can severely harm the business of the content distributor. Such an event is greatly feared and its risk must be known to be very low.

VI. EXPERIMENTS

In the following we present results on the basis of 4 different 720p sequences (Canal, Depart, Ebu, Elephant) with 250 frames. The sequences reflect different natural content, as well as one artificially generated sequence (Elephant). The sequences have been encoded with H.264 CAVLC with a Qp of 13 (very high quality). The lower the quality (higher Qp) the more macroblocks are coded in P16x16 (as the frames become smoother and larger partitions more efficient). Thus our approach works even better for lower quality video. An I(BP)* prediction structure with non-reference B-frames is employed, which is a common and reasonable configuration. The Blu-ray specification even requires B-frames to be nonreference frames. Only P16x16 macroblocks (having no subpartitions) of B-slices have been employed for watermarking. The search range for candidate MVD changes has +/-16 in each direction and the MVD change with the maximum feature difference below the distortion threshold was selected. Furthermore we rejected MVD changes that modified the average luminance feature by less than 0.25.

Our watermarking algorithm can be employed with different values of the embedding distortion parameter MbDist (distortion measured in MSE). Both the MbDist and the source material (video) have an impact on the number of bits that can be embedded (see table II for results on our test sources). The encoder decides on the basis of the source video which macroblocks are encoded as P16x16 blocks. The analysis stage of watermark embedding only chooses MVD changes which result in a macroblock distortion (in MSE) that is below MbDist. Therefore the reduction of MbDist severely reduces the set of candidate MVD changes for highly textured sequences (natural video content, especially for the Depart sequence), while the reduction of MVD changes is far less severe for computer generated content, such as the Elephant sequence (because of the smoothness of the computergenerated content). For highly textured sequences slight spatial shifts (the result of a MVD change) lead to higher MSE distortions.

In the next section we present evidence that even the highest embedding strength offers very good / excellent quality.

A. Quality Evaluation

A subjective experiment was conducted, 42 observers were enrolled, their acuity was checked as well as normal color vision. The ACR (absolute content rating) protocol was used. For this protocol, a single video sequence is displayed in the center of the screen, and the observer is asked for a quality score after every displayed sequence. The resolution of the tested video sequences was 1280 × 720. The quality score was (5: Excellent, 4: Good, 3: Fair, 2: Poor, 1: Bad), MOS were computed across the 42 observers, as well as standard deviations for every content across observers, the maximum standard deviation was below 1 (0.93). During the experiment, 56 videos were evaluated, the database was built as follows. Among the four input sequences three were collected from the VQEG datasets (Canal, Depart and Ebu) and one sequence was an artificial (cartoon) sequence (Elephant). Every sequence was either watermarked and re-encoded, or only re-encoded. The watermarking technique was presented in section V. Six quantization parameters were used (Qp = 24, 28, 32, 36, 40, 44) for both watermarking and re-encoding scenarios. The original input sequences and watermarked sequences were also considered in the experiment (using a Qp of 13). Overall four original sequences have been subjected to two distortions (watermarking and coding or coding only) and each resulting sequence has been re-encoded with 7 quantization parameters. In summary this results in a dataset of 56 sequences. Each sequence was 10 seconds long (250 frames). For each observer, the experiment duration was in between 15 and 20 minutes.

The main objective of splitting the dataset into two parts: watermarking and coding was to analyse any perceptual quality loss due to the watermark embedding. To this end, figure 5 shows a histogram representing the difference between watermarked and coded sequences.Positive x-axis values means that the watermarked sequences presented a higher quality score than the coded version (and negative values along the x-axis means the coded sequence had a higher quality score). The yaxis simply counts the number of occurrences for all observers and for all sequences. As we can notice on this figure, the histogram bins are symmetrically distributed around zero, The symmetry of the histogram indicates that the differences in quality perception between watermarked and non-watermarked videos is random, i.e., there is no difference in the perceived quality of watermarked or non-watermarked videos.

Figure 6 shows the Mean Opinion Score as a function of the quantization parameter for all tested sequences. The gray lines represent the differences between marked and coded sequences. It is interesting to notice that these differences are centered around zero, which means that depending on the sequences and the Qp, the allocated quality score could either be higher for the watermarked sequence or for the coded sequence. On this figure, the symbols (arbitrarily positioned at Qp=22) represents the original marked (squares) and coded (diamonds) sequences.

Moreover, five Objective Quality Metrics were tested on this subjective dataset (PSNR, SSIM [START_REF] Wang | Image quality assessment: From error visibility to structural similarity[END_REF], CPA1 [START_REF] Carosi | Toward a simplified perceptual quality metric for watermarking applications[END_REF], CPA2 [START_REF] Pankajakshan | A multi-purpose objective quality metric for image watermarking[END_REF] and VIF [START_REF] Sheikh | Image information and visual quality[END_REF]). The performances of the metrics were assessed in terms of wRMSE, RMSE, Rank correlation, Outlier Ratio, Kappa coefficient, and Linear correlation. Table III provides the metrics performances for all five metrics. It is obvious from table III that the VIF metric outperforms all others for the six tested performances tools, and PSNR exhibits the worst overall performances. Thus, in the following our analysis will focus on these two metrics.

Figures 7(a) and 7(b) respectively show the MOSp for PSNR and VIF as a function of Qp. We can observe that neither PSNR, nor VIF can notably differentiate coded sequences and watermarked ones. It is interesting to notice that both metrics disagree concerning the assessment of the Elephant sequence (computer generated sequence). A further analysis showed that for all tested metrics, except VIF, the predicted Mean Opinion Score (MOS) marked "canal" marked "depart" marked "ebu" marked "elephant" coded "canal" coded "depart" coded "ebu" coded "elephant" diff "canal" diff "depart" diff "ebu" diff "elephant" Marked Coded Fig. 6. Subjective mean opinion scores for three coding parameters (different Qp) scores for the Elephant sequence were seen as presenting a significantly higher perceptual quality than the other 3 sequences. This explains the overall low metrics performances shown in table III. This particular behavior is clearly visible on figure 8(a) representing the PSNR plotted as a function of the MOS. For low to good quality, the PSNR values for the Elephant sequence are about 9dB higher than the remaining sequences, whereas the MOS for this sequence was slightly below others (see figure 6). Such a behavior is not apparent for the VIF metric (see figure 8), which presents a linear distribution of MOS versus MOSp, and as we have seen above, is capable of discriminating the Elephant sequence as having an overall lower quality.

Given our subjective results we conclude that watermarked marked "canal" marked "depart" marked "ebu" marked "elephant" coded "canal" coded "depart" coded "ebu" coded "elephant" diff "canal" diff "depart" diff "ebu" diff "elephant" Marked Coded (b) VIF Fig. 7. OQM predictions on coded or watermarked sequences for various Qp content can not be visually distinguished from not watermarked content (when played as video). This may come surprising as the allowed embedding distortion is high with a MbDist of 100 in terms of MSE. However, the watermarking approach implicitly takes advantage of temporal masking effects, as due to the algorithm design the watermark is always embedded in high motion areas, in which distortion is perceived less pronounced by human observers.

B. Robustness Evaluation to H.264 and H.263

The robustness of our watermarking algorithm highly depends on the embedding strength as defined by the parameter MbDist as the number of embedded bits is primarily determined by this parameter. Recompression is the main focus of our robustness evaluation, most importantly recompression with H.264 and H.263. The employed software for recompression was x264 2 (ultrafast, varying quality parameter Qp) for H.264 compression and ffmpeg 3 (vcodec=mpeg4, varying quality scale Qs) for H.263 compression. The chosen quality ranges correspond to qualities from excellent to bad for both H.264 and H.263. The false positive probabilities in the figures 10, 12, 15 and 16 have been derived from the Binomial distribution [START_REF] Eggers | Informed Watermarking[END_REF]. For watermarked content the false negative probabilities have been derived from a Gaussian distribution which has been fitted to the data generated by 50 different watermarking keys [START_REF] Eggers | Informed Watermarking[END_REF], [START_REF] Cox | Digital Watermarking and Steganography[END_REF] (in figures 15 and 16).

Figure 9 plots the detector response against the Qp employed in x264 compression. Results are given for 4 different sequences and different embedding strengths (MbDist). A detection threshold has to be chosen such that it separates the detector responses of un-watermarked content (dashed blue lines in fig. 9) and watermarked content (solid red lines in fig. 9). For MbDist 100 and 25 the selection of detection threshold that separates un-watermarked from watermarked content is obviously possible, and even for a MbDist of 4 the detector response for an un-watermarked content is always below the detector response of the same watermarked content. While this figure can only give a first impression, a better interpretation of the detector values is obtained if we consider the associated false positive probability for the obtained detector response. Figure 10 plots the exponent of 1/pfp in basis 10, i.e., a value of 8 corresponds to a false positive probability of 1/10 8 , against the Qp. Note that the negative exponents of the pfp have been clipped at 8. We notice high robustness to H.264 compression even for very bad quality and even for low embedding strengths, only at MbDist=4 the detection performance decreases significantly (although even this limited robustness may be sufficient for the protection of high quality content). We further have to point out that the ultrafast settings of the x264 encoder can be considered the worst case, as these settings introduce heavy distortions (but are fast). Thus the presented results correspond to a worst case scenario for robustness, other encoders (or other settings for x264) preserve a better quality and thus the watermark is more reliably detected.

Even better is the robustness to H.263 compression as summarized in similar figures (see fig. 11 and12).

However, single detector responses only represent a single sample from a random experiment; a more thorough analysis has to draw several samples from the random experiment in order to enable a statistical analysis of the underlying distributions. Given that the distortions are computationally expensive (repeated H.264 and H.263 encoding / decoding) the more extensive analysis focuses on medium quality recompression which can be considered the default case in many application scenarios (e.g., illegal file sharing). In the following experiments 50 watermarking keys have been employed both for detection in content watermarked with the same key (H 1 ) and content that has not been watermarked with the same key (H 0 ). The experiments have been conducted for different embedding strengths (MbDist). The figures 13 and 14 contain histograms of the detector response under H 0 (on the left, detector responses distributed around 0) and H 1 (on the right). The resulting distribution under H 0 follows approximately a Gaussian distribution, the parameters can be either estimated on the basis of the obtained detector responses (the dashed line in the histograms) or exactly by the Binomial distribution (the solid line with upward triangles). We notice that the prediction on the basis of the Binomial distribution is very close to the fitted Gaussian distribution. The solid line with downward triangles is the fitted Gaussian distribution for the detector responses of watermarked content. If the embedding strength is reduced the detection performance slowly decreases, i.e., the distributions of Z under H 0 and Z under H 1 are less and less separated. However, even for an embedding strength of 25 the distributions are clearly separated, and for a embedding strength of 4 many sequences still present well separable distributions. The same information (as contained in histograms) can be plotted in ROC (receiver operation curves). The results are only shown for the Depart and the Elephant sequence as the results of these two are the most different, the performances of the other sequences are in between. The different behaviour of these two sequences is due to the different video characteristics, on the one hand the relatively smooth computer-generated Elephant sequence on the other hand the highly textured Depart sequence, with high, but local and independent motion (it contains a sequence of a cross country running race, each runner moves independently of the others). For the Depart sequence there are simply too few MVD changes that result in a distortion below an MSE of 4. On the other hand the MSE penalty of an MVD change in a smooth sequence, such as Elephant, is far less pronounced, resulting in many watermarkable blocks and thus a higher robustness.

Figures 15 and figures [START_REF] Sheikh | Image information and visual quality[END_REF] show ROC plots for common recompression attacks with H.264 and H.263. In the ROC plots the exponent (of basis 10) of the false positive probability (xaxis) is plotted against the false negative probability (y-axis). Thus an x-axis value of -8 corresponds to a probability of 10 -8 . The closer an ROC curve is to the axes the better is the performance of the associated watermarking scheme (the proposed algorithm with different parameters of MbDist). We plotted results starting from a very high false positive probability of 10 -1 to a a low false positive probability of 10 -9 , which should contain results for most practical systems. The schemes with MbDist=100 and MbDist=25 exhibit excellent performance for all sequences and both distortions (see figures 15 and 16). Only for a MbDist=4 problems are encountered as too few candidate blocks are found. The number of watermarkable blocks depends on the source characteristics, MVD changes in highly textured content result in an higher MSE. In a practical system, one would simply need to embed the watermark into more frames, i.e., a longer sequence.

In conclusion, at a MbDist of 25 and above we can extremely reliably detect the presence of the watermark even for highly compressed sequence (both H.264 and H.263) and even at lower embedding strength many sequences show a very good detection performance.

It is also notable, that the detector responses do not change significantly for the different embedding distortions, only the number of embedded bits increases significantly with higher embedding distortions. The higher the number of embedded bits the lower the detection threshold can be chosen for a given false positive probability. Thus lower embedding distortions solely require to watermark more frames to achieve higher robustness.

C. Watermarking Attacks

In the following we present results for a relevant subset of the Checkmark watermarking evaluation framework [START_REF] Pereira | Second generation benchmarking and application oriented evaluation[END_REF] and additionally we present first results for a targeted attack on our watermarking system. As the embedding distortion mainly influences the number of embedded bits, only results for an embedding distortion of 100 (MbDist) are given in tables IV and V. Although the attacks significantly reduce the detector responses, the decrease is not a problem as long as the number of embedded bits is sufficient. The number of embedded bits can be simply increased by watermarking more frames (our results are only for 10 seconds video clips).

While the Checkmark attacks are generic attacks against watermarking systems, an attacker might exploit the specifics of a watermarking system to design a targeted attack. For our approach an attacker can analyze the H.264 bitstream and identify candidate macroblocks, i.e., macroblocks which have equal-length MVD options. As an attacker does not have access to the original, she can not determine whether the macroblock has been considered watermarkable. The MVDs of candidate macroblocks then can be set to a canonical value, e.g., the minimum MVD value. This attack removes the watermark, but also introduces heavy distortions, i.e., the attacked video suffer from flicker and many frames show a heavily reduced quality due to blocking artifacts. Figure 17 shows the result of the attack on the Elephant sequence.

D. Comparison to Previous Work

The comparison to previous work focuses on the algorithms by Zou and Bloom [START_REF] Zou | H.264/AVC stream replacement technique for video watermarking[END_REF], [START_REF] Zou | H.264/AVC substitution watermarking: a CAVLC example[END_REF], [START_REF] Zou | H.264 stream replacement watermarking with CABAC encoding[END_REF], which are the only proposals that satisfy similar requirements, i.e., offer structurepreservation and substitution embedding for H.264. Neither the implementation nor the test data (a clip from the action movie "Independence Day") of these approaches could be made accessible by the authors. Therefore we are not able to perform a rigorous experimental comparison with exactly the same settings for all algorithms. However, we provide experimental results for our approach for four very different video sequences, which helps to put the results into perspective and enable a fair comparison. All the test data of our evaluation are publicly accessible (ftp://ftp.ivc.polytech.univnantes.fr/IRCCyN IVC H264 Watermarking Structure Preserving/) and thus future proposals can rigorously compare their approaches to our proposal.

Compared to Zou and Bloom's approaches [START_REF] Zou | H.264/AVC stream replacement technique for video watermarking[END_REF], [START_REF] Zou | H.264/AVC substitution watermarking: a CAVLC example[END_REF], [START_REF] Zou | H.264 stream replacement watermarking with CABAC encoding[END_REF] our approach is superior in terms of the number of watermarkable blocks (see table VI). Thus fewer frames are needed to embed a watermark. Alternatively the robustness of our approach can be improved (a lower threshold can be selected for the same probability of alarm).

The comparison of the robustness on bit embedding level reveals that the performance is rather similar, while our robustness criterion (the average luminance feature difference must be larger than 0.25) leads to higher correlations for downsizing, the effect of downsizing and compression are almost the same (see table VII). A special case again is the computer generated Elephant sequence, which can be compressed very efficiently and thus the highest correlations are observed with this sequence.

The number of embedded bits has a tremendous effect on the threshold selection (for a fixed false positive probability) or the false positive probability (for a fixed threshold). Figure 18 plots the thresholds for different probabilities of false alarms and for the different approaches against the number of frames. As distortions affect the different approaches almost similarly this allows a fair comparison of the performance of the approaches. The lower the threshold the more robust is the watermarking algorithm against distortions. Our algorithm requires significantly lower detection thresholds compared to the algorithms of Zou [START_REF] Zou | H.264/AVC substitution watermarking: a CAVLC example[END_REF], [START_REF] Zou | H.264 stream replacement watermarking with CABAC encoding[END_REF].

VII. CONCLUSION

The proposed H.264/CAVLC watermarking algorithm enables structure-preserving H.264 watermarking. Due to the separation of embedding in an analysis and and a substitution stage, it is able to efficiently generate numerous video files

Fig. 1 .

 1 Fig. 1. Watermark embedding

Fig. 5 .

 5 Fig. 5. Watermarked versus coded: differences in opinion scores

2

  x264 0.85.1448 Ubuntu 2:0.85.1448+git1a6d32-4 3 FFmpeg version SVN-r0.5.1-4:0.5.1-1ubuntu1.2, Copyright (c) 2000-2009 Fabrice Bellard, et al

Fig. 8 .

 8 Fig. 8. OQMs plotted as a function of the mean opinion score

Fig. 9 .Fig. 10 .Fig. 11 .Fig. 12 .Fig. 13 .Fig. 14 .Fig. 15 .Fig. 16 .

 910111213141516 Fig. 9. Detector response for varying Qp of x264 (720p, 250 frames) for watermarked content (red) and not watermarked content (blue)

Fig. 17 .

 17 Fig. 17. Visual examples of an attack based on a bitstream analysis

Fig. 18 .

 18 Fig. 18. The thresholds for the approaches of Zou and to our algorithm plotted as a function of frames for a false positive probability of 10 -7

TABLE VI WATERMARKABLE

 VI B L O C K S P E R F R A M E O F 1080PVIDEO, THE RESULTS OF ZOU'S CAVLC [4] AND ZOU'S CABAC [5] COMPARED TO OUR APPROACH FOR DIFFERENT SEQUENCES

	Zou & Bloom		Our approach
	CALVC	CABAC Canal	Depart	Ebu	Elephant
	0.625	1.319	17.672	41.196	57.420	18.820

TABLE VII AVERAGE

 VII CORRELATION FOR ZOU'SAPPROACH[START_REF] Zou | H.264/AVC substitution watermarking: a CAVLC example[END_REF] AND OUR ALGORITHM

	Attack	Zou CAVLC (Full HD)	Our (Canal Full HD) Our (Depart)	Our (Ebu) Our (Elephant)
	No Attack	0.9685	1.0000	1.0000	1.0000	1.0000
	Downsize to 960x540	0.8133	0.9995	0.9998	0.9988	1.0000
	Downsize to 480x270	0.2778	0.9488	0.9586	0.9669	0.9974
	Downsize to CIF	0.1804	0.8497	0.8572	0.8942	0.9795
	Downsize to CIF, 1M	0.1148	0.2643	0.2403	0.2108	0.8826
	Downsize to CIF, 780K	0.1181	0.2489	0.2088	0.1710	0.8826
	Downsize to CIF, 300K	0.1026	0.0878	0.1043	0.0882	0.6272


F Mazza

M P Da Silva

LUNAM P Le Callet

Investigating Electrophysiology for Measuring Emotions Triggered by Audio Stimuli

Keywords: Speech, Image and Video Processing: Models for Signal and Image Processing Multimedia quality, affective evaluation, electrophysiology, EEG, LDPM

Multimedia quality evaluation recently started to take into account also analysis of emotional response to audiovisual stimuli. This is especially true for quality of experience evaluation. Self-assessed affective reports are commonly used for this purpose. Nevertheless, measuring emotions via physiological measurement might be also considered as it could limit the effects of cognitive bias due to self-report following the rule that your body cannot lie. In this paper we first review in the context of emotion assessment, common physiological measurements such as cardiac and respiratory rhythms, Electroencephalography (EEG) and Galvanic Skin Resistance (GSR); more advanced techniques as infrared thermography or blood perfusion with laser Doppler flowmetry. Then we present some results of electrophysiological measures related to the evaluation of emotions triggered by sounds.

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I. INTRODUCTION

Affective research has become from mere theoretical discussion one of the most active topic in recent research [START_REF] Picard | Affective Computing: From Laughter to IEEE[END_REF]. Emotion evaluation can be useful for many aspects. For example, understanding users' state of mind can be very valuable in researches where the user itself plays an important role in evaluation models, as it is becoming with research on quality of experience [START_REF] Callet | Qualinet White Paper on Definitions of Quality of Experience, European Network on Quality of Experience in Multimedia Systems and Services (COST Action IC 1003)[END_REF]: perceived quality is related to a broader evaluation taking into account multiple influence factors. Between them human factors, taking into account user related context and notably user emotional state. User's affective state evaluations have then started to be included in perceptual models, as emotions play an important role in how we perceive the world around us and how we react to it [START_REF] Damasio | Descartes' Error: Emotion, Reason, and the Human Brain[END_REF]. Different ways of measuring emotions have been studied. For long time moods and emotions have been evaluated through questionnaires developed by psychologists or other non verbal instruments such as behavioural analysis [START_REF] Desmet | Measuring emotions; development and application of an instrument to measure emotional responses to products[END_REF]. The problem with those systems is inherent to the highly subjective nature of the topic but also to the less obvious problem of describing correctly what we feel. However, our emotions affect not only our perception but also our physiology, having reflections on the nervous system. Evidence of emotion reflection on physiological activity has been found both in central and peripheral nervous system. Distinctive patterns of autonomic nervous system (ANS) activity have been found for some emotions [START_REF] Ekman | Handbook of Cognition and Emotion, Basic Emotions[END_REF]. As 'our body can't lie', electrophysiology has been later adopted to evaluate emotional impact achieving remarkable results. Many differently kind of electrophysiology measures have been studied in the field, both related to the Central (CNS) or Peripheral Nervous System (PNS) [START_REF] Chanel | Emotion assessment from physiological signals for adaptation of game difficulty[END_REF]. Considering this last case, as body temperature is controlled also via blood perfusion in tissues, we expect that blood perfusion measure reflects emotional responses. In this paper we focus on first experiments with blood perfusion measurement through Laser Doppler measurements -shortly Laser Doppler perfusion monitoring or LDPM -that has been very few times used in affective research. However this technology has been positively employed for affective research with stimuli other than multimedia, notably water taste [START_REF] Haese | Tastes and odors of water -Quantifying objective analyses : a review[END_REF]. Our aim is to evaluate the use of this technology alone for affective research and check how it relates with other better known alternatives.

II. ELECTROPHYSIOLOGY AND EMOTIONS

A large literature is present regarding electrophysiology measures related to emotions felt presenting multimedia stimuli. Often researches focus on correlating physiological patterns with emotion self assessments, used as ground truth.

Brain activity, related to CNS, is directly related to emotions and many researches have been carried out in this field. EEG is among the most used measures in affective research and even if mechanisms related to emotions in the brain are still to be completely understood, research on EEG is now quite advanced. Psychophysiological studies underline how frontal brain lobes are directly related to emotion [START_REF] Niemic | Studies of Emotion: A Theoretical and Emperical Review of Psychophysiological Studies of Emotion[END_REF] [START_REF] Bos | EEG-based emotion recognition: The influence of visual and auditory stimuli[END_REF]. EEG band power are the main features used as input for machine learning systems. Four main bands are commonly used, called alpha beta delta and gamma, based on different signal patterns appearing during different brain activities. Signal classifications using these and other features have been adopted successfully, in particular Neural Networks [START_REF] Bos | EEG-based emotion recognition: The influence of visual and auditory stimuli[END_REF] or gaussian processes [START_REF] Zhong | Classifying EEG for brain computer interfaces using gaussian processes[END_REF]. Remarkable results have been obtained in [START_REF] Nie | EEGbased emotion recognition during watching movies[END_REF], where positive and negative emotions are classified with Space Vector Machines achieving accuracies slightly below 90%. Magnetic Resonance Imaging has also been adopted to evaluate brain reactions to affective audio stimuli [START_REF] Viinikainen | Representation of perceived sound valence in the human brain[END_REF]: strong relationship have been found between sound emotional value and blood oxygenation in brain areas related to emotional reactions, as amygdala and prefrontal cortex.

As CNS manages all biological basic functions supporting life like cardiovascular activity or respiration, emotion's influence on biosignal is likely to be found also on PNS. Evidence of this assumption has already been found and different biosignals related to PNS have been measured in relation to emotions. These measures usually present the advantage of being less invasive than the ones of CNS. Cardiorespiratory activity is probably the most basic physiological activity related to emotions. Distinct patterns have been found and associated to basic emotions felt. In [START_REF] Rainville | Basic emotions are associated with distinct patterns of cardiorespiratory activity[END_REF] researchers investigated cardiorespiratory activity during experiences of different emotions. Those emotions have been elicited asking subjects to disclose a personal strongly affective experience. Difference between electrophysiological patterns have been found using a multivariate analysis approach and the study underlines how peripheral physiological activity is associated with different emotions. Many other researches have been focused to cardiovascular activity related to emotions, especially in medicine. A comprehensive review can be found in [START_REF] Kreibig | Cardiovascular, electrodermal, and respiratory response patterns to fear-and sadness-inducing films[END_REF]. Another measure used is Galvanic Skin Resistance, expecially used for reactions to stress [START_REF] Wu | Exploring the use of physiology in adaptive game design[END_REF] but also related to cognitive load during certain tasks [START_REF] Nourbakhsh | Using Galvanic Skin Response for Cognitive Load Measurement in Arithmetic and Reading Tasks[END_REF]. Electromyography has been used too in conjunction with GSR as in [START_REF] Nakasone | Emotion recognition from electromyography and skin conductance[END_REF] or alone as with facial electromyography in [START_REF] Niedenthal | Embodiment of emotion concepts[END_REF] while participants judged emotional and neutral concepts. Recently pupil diameter has been used too [START_REF] Ren | Affective Assessment by Digital Processing of the Pupil Diameter[END_REF]. Multimodal approaches in affective research have been approached too, using multiple electrophysiology measurements at the same time, as in [START_REF] Koelstra | DEAP: A Database for Emotion Analysis; Using Physiological Signals[END_REF]. This research provides also the collection of physiological signals online, providing a dataset consisting of multiple measurements as EEG, GSR, respiration amplitude, blood volume, face recordings and electrooculogram taken while users were watching audio-video stimuli.

As mentioned before, temperature is another monitored parameter. Infrared thermography has been used to demonstrate reflexes due to emotional video stimuli [START_REF] Kistler | Fingertip temperature as an indicator for sympathetic responses[END_REF]. In this case, the sympathetic vasoconstriction in forearms was monitored. A correlation with stimuli presentation was found, demonstrating local temperature changes. In detail, the video stimuli presented a thriller scene and the temperature decreased during this stimuli. The same principle has been recently adopted with fingertip temperature to implement first steps toward an automatic recognition system [START_REF] Shivakumar | Emotion Recognition Using Finger Tip Temperature: First Step towards an Automatic System[END_REF].

The evaluations related to the cardiovascular system for emotional impact were only heart pulse and blood pressure of systemic circulation, . At the best of our knowledge until now blood flowmetry alone has never been used for multimedia affective research, as it has used only in coordination with thermal imaging [START_REF] Kistler | Fingertip temperature as an indicator for sympathetic responses[END_REF]. Good results have been instead achieved for water taste stimuli in [START_REF] Haese | Tastes and odors of water -Quantifying objective analyses : a review[END_REF]. In this paper we want to explore the possibilities given by using blood perfusion monitoring in hands fingertip without thermal imaging.

III. LASER DOPPLER PERFUSION MONITOR

Laser Doppler Perfusion Monitoring (LDPM) is a technique used in medicine for studying the perfusion of blood in microcirculation of tissues. It is widely adopted for analyzing tissues damaged by heat as in burn assessment or in necrotic pathologies. In general, laser doppler velocimetry adopts the shift in the frequency of a low power laser's reflected light as a measure of the quantity and velocity of particles in fluids [START_REF] Durst | Principles and practice of laser-Doppler anemometry[END_REF]. This is why it is also known as laser doppler flowmetry or velocimetry. The principle is the following: a laser light beam of a fixed wavelength is emitted toward the fluid, so that the incident light is reflected by particles in it. A receiver measures the light reflected at a fixed angle. Frequency of reflected light is shifted hitting moving particles due to the Doppler Effect. As part of emitted light is absorbed and diffracted, the measure takes into account light reflected by an area defined by the characteristics of the laser frequency and of the material under test. In our device's probe, a 780 nm laser light is emitted 0.25 mm far from the receiver; considering the average composition of surface skin, we are able to measure blood perfusion 1 mm beneath skin. Blood perfusion is directly related to both the amount and speed of particles in the capillaries, and it's influenced by many different factors, like position, age, temperature, health, heart rate and blood pressure. As these last physiological factors are ruled by the autonomous nervous system, we are investigating if this measurement links with users psychological reactions. LDPM shows fluctuations in flow's speed. This allows to measure homeostasic reactions like vasoconstriction and also heart beats; in the first case signal shows a decay due to lowering of flow, while in the second faster oscillations will be summed to the signal baseline. With more accuracy and sampling, also heart valves opening/closing can be seen. A deeper analysis of laser Doppler perfusion signal frequencies measurable from human skin is present in [START_REF] Kvandal | Low-frequency oscillations of the laser Doppler perfusion signal in human skin[END_REF], also during thermal tests [START_REF] Maniewski | Spectral analysis of laser-Doppler perfusion signal measured during thermal test[END_REF]. This measurement gives an absolute value -in Perfusion Units (PU) -of the irroration of tissue due to microcirculation. As said, perfusion is related to reflected light; the reflected light is related both to the velocity and quantity of particles. Two different measures are then available, called respectively Velocity and Concentration of Moving Blood Cells (CMBC). The perfusion is given by their product. At the moment, to the best of our knowledge, LDPM has been used in affective research with multimedia stimuli only once as a complementary measure, to check the presence of vasoconstriction due to a stimuli [START_REF] Kistler | Fingertip temperature as an indicator for sympathetic responses[END_REF]. Recently skin blood flow has been monitored with LDPM in affective research for other kind of stimuli, notably tastes and odours of water [START_REF] Haese | Tastes and odors of water -Quantifying objective analyses : a review[END_REF]. In this work the main measurement adopted was infrared thermography on fingertip temperature and perfusion measurements where taken jointly LDPM and also blood pressure.

IV. METHODS

To evaluate LDPM technology for affective research we conducted an experiment aimed at collecting blood perfusion measures from users listening to affective sounds. We then tried to classify recorded signals respect to class of stimuli proposed. To do this, as it is one of the first studies of LDPM for affective stimuli, no reference methodology exists at the best of our knowledge. This is why, as a first approach, we adopted a similar methodology and data analysis as in a previous affective experiment we conducted adopting EEG; data has been collected within a broader multimodal joint research related to quality of experience in the scope of Qualinet community1 [START_REF] Moor | Chamber QoE A Multi-instrumental Approach to Explore Affective Aspects in relation to Quality of Experience[END_REF]. That research adopted audio video stimuli as EEG technology has been deeply researched for affective purposes and interactions have been described in different conditions. Regarding the LDPM experiment instead we decided to use simple affective stimuli,as mechanisms underlying the relation between blood perfusion and emotions felt are not yet well known and described. We adopted a single modality -only audio -as we still don't know if there is any effect regarding multimodality audio video. We've adopted the International Affective Digitized Sounds (IADS) database, provided under request by the NIMH Center for Emotion and Attention (CSEA) at the University of Florida [START_REF] Bradley | The International Affective Digitized Sounds (2nd Edition; IADS-2): Affective ratings of sounds and instruction manual[END_REF] as done on others affective researches [28] [12]. This database consists of many different pure sounds -without any speechof different nature, evaluated subjectively for their emotional impact. The latest version of this database take into account 167 different sounds of 6 seconds length in average, rated each one from at least 100 participants. Emotional impact has been assessed with the Self Assessment Manikin (SAM), providing a rate in the PAD space. We selected a subset of this database to limit experiment duration, as longer times are more likely to cause stress and/or boredom to the user, impairing affective assessment. Subset has been constructed choosing sounds the more possibly spaced on the PAD space. Clustering and selection of representants has been adopted to restrict sound number, as done in [START_REF] Viinikainen | Representation of perceived sound valence in the human brain[END_REF]. Before clustering the space we restricted the number of samples selecting the ones with lower standard deviation in assessments. For clustering we adopted the KMean algorithm. At first we empirically choose 5 different regions in the PAD space looking at the sound distribution and selected the three sounds closest to cluster centroid to represent it; ANOVA analysis on the three separate dimensions has been run to avoid taking outliers of a cluster. As we do not have raw self assessment data -not provided with the IADS database -we adopted the procedure described in [START_REF] Cohen | Calculating a factorial ANOVA from means and standard deviations[END_REF] using directly mean value and standard deviation of sounds evaluations. The whole procedure brought to underline two big group of sounds, that we indicate as A and B respectively, as shown in figure 1, of lower or higher affective impact. In any case, we rely completely on the effectiveness of chosen stimuli. We suppose that the user actually perceives the content as expected, eliciting emotions, but this could not be the case for all or a part of the stimuli.

Blood perfusion has been measured only via Laser Doppler, using a medical grade device made by Perimed, the PeriFlux System 5000 equipped with a PF 5010 LDPM unit. Data has been acquired through the official software provided, sampled at 125 Hz. Blood perfusion has been measured on left hand index fingertip; while it was possible to measure it also in other points, we've chosen this point as previous research conducted on hands temperature reaction to affective stimuli already showed perfusion changes in reaction to sympathetic responses. Probe was positioned at finger's center.

For each test run we randomized cluster order presentation and sound order in each cluster, as we did not want bias introduced by presentation order or by any cumulative effect on emotion that can arise. We saved for each trial the order on a file in order to further process our data.

Each cluster, made of three representants, has been presented once to each subject. A fixed amount of time has been waited before providing the next stimuli burst. The purpose During pre-tests, we empirically determined that a pause of 10 seconds was in average enough to let the user relax and restore a baseline in physiological signals to start from. It has to be noticed that this baseline can differ from the one at the beginning, as the overall state of the user can change, although the randomization at the beginning mitigate possible bias or accumulation effects in the user. We noticed also that more seconds are not likely to restore this baseline easier, as fluctuations arise again. Our hypothesis is that a longer time can cause the user to focus on something else and this can provide measurable reactions on the user that can bias the experiment. For example, a longer period may induce the user to think about something wrong with the experiment or on how much time has passed and think on what he has to do next, causing a reaction dependent on his state of mind.

26 people, aged between 23 and 30, participated in the experiment. People have been previously informed about the experiment, the kind of stimuli used, the time required and the anonymity of results before participating. Only the exact content of stimuli and purpose of the experiment have not been revealed in order not to false reactions during listening. The experiments took place in a dedicated experiment room in controlled conditions; we've chosen an un-noisy environment and we used an high quality headset made by AKG. Audio volume was fixed on an audible level for all users and was not normalized as we relied completely on the sounds prepared in the IADS database. Temperature has been monitored, adopting a warm ambient with a temperature of 22 +-1 C, as thermal factors such as core and skin temperatures impacts deeply on the sweat rate and skin blood flow [START_REF] Kondo | Thermoregulatory adaptation in humans and its modifying factors[END_REF] [START_REF] Maniewski | Spectral analysis of laser-Doppler perfusion signal measured during thermal test[END_REF]. We introduced the subject in this room approximately 6 minutes before starting the listening in order to allow them to adapt to this temperature; many parameters impacts on thermoregulation time, but researches show adaptation times inferior to this in similar conditions [START_REF] Kondo | Thermoregulatory adaptation in humans and its modifying factors[END_REF]. Moreover, we adopted this relax period because also physical activity impacts on perfusion value [START_REF] Kvernmo | Spectral analysis of the laser Doppler perfusion signal in human skin before and after exercise[END_REF].

V. DATA ANALYSIS AND RESULTS

We started analysis adopting techniques already used in different electrophysiology measurements, notably as with our EEG experiment previously cited, as there is no literature regarding LDPM in detail for affective research. We focused our attention directly on the perfusion signal instead on only Velocity or CMBC signals as the first is related to both of them and we don't know which one can be more representative of an emotion. Signals have been inspected manually in order to check for clear errors or impairments, as due to data communication errors or probe shifts, ending in removing a user from the dataset as probe had a discontinuous contact with user's finger. Other smaller impairments have been successively removed manually. Signals have been aligned cutting unneeded seconds from beginning and end of recordings, belonging to setup and post-test moments. This visual inspection showed also a large variation between signals, showing a different degree of reaction between users; only in few cases very small or no variations from baseline were present. It is not possible to say if this is only due to their personal variation in perfusion or to an higher reaction to stimuli. In some cases a slow 'fall-rise' pattern is observed soon after a stimuli. However delay and amplitudes are strongly subjective; in any case no stimuli elicited a fall-rise pattern longer than 3 seconds until now. However, considering pattern features as mean, standard deviation or derivate, the analysis carried out did not underline a strong correlation stimuli/observed pattern. A first simple analysis to carry is to find if with only the LDPM we can detect the presence of a stimuli, that is to say if the stimuli perception provoked a variation on signals baseline. To analyze mathematically perfusion signals evolution after a sound stimuli we the filtered heart rate components, visible on perfusion signal, and higher frequencies. Low frequency oscillations are confirmed on Laser Doppler Flowmetry of blood [START_REF] Kvandal | Low-frequency oscillations of the laser Doppler perfusion signal in human skin[END_REF] and our analysis shows 99% of the power distributed below 4 Hz. Hearth rate pulses have been removed with a notch filter as here we are not interested in heart pulse measure to our affective evaluation. Signals have then been low pass filtered, decimated and z-scored. Starting from previous observations we selected signal features to use on a machine learning algorithm. Our aim here is to check if we can discriminate between user state while listening to a sound belonging to cluster A or cluster B previously described. Signal has been windowed in order to separate reactions belonging to different sounds. Power of different frequencies have been extracted from perfusion signals based on proposed features in literature, related to other physiological responses, as in [START_REF] Bos | EEG-based emotion recognition: The influence of visual and auditory stimuli[END_REF] [START_REF] Koelstra | DEAP: A Database for Emotion Analysis; Using Physiological Signals[END_REF]. We adopted in detail a multilayer perceptron, feedforward backpropagation, with one hidden layer, tuning it accordingly; 15% of data has been used for the validation stage. Classification shows accuracy better than chance, as presented in confusion matrix in figure 3. Results are impaired mostly from erroneous classification of second cluster, relative to higher values in PAD space, as a low value one. The other one instead, although presents some misclassification, is better recognized. These results are less performing compared with the ones of our previously cited EEG experiment, from which we adopted methodology and data analysis. In that experiment, adopting as for LDPM two classes for low and high impact stimuli, we achieved an accuracy in correct classification of 82% for the first and 76% for the second class.

VI. DISCUSSION

Research on affective research, adopting different electrophysiological measurements, demonstrated notable results. However some challenges are still open. Different methodologies may be suitable with different stimuli or subjects, as reactions may be inherently different. Multiple technologies can be used even at the same time, as they can be complementary. These elements give space for improvements. In this paper we showed preliminary results on affective research investigating Laser Doppler perfusion monitoring. To the best of our knowledge, this is the first time this methodology is used alone for affective evaluations with multimedia stimuli. We conducted a test with affective stimuli recording LDPM, allowing us to take confidence with the instrument, the protocol and to gather a set of data measurements to work with, identifying main challenges. While results obtained are encouraging, lot of work is still to be done to reliably adopt LDPM alone with affective multimedia stimuli. Multiple elements in our experiment can contribute to this outcome. Choice of probe position and small variabilities of its position due to differences in users fingertips; circadian rhythms influencing blood pressure baseline; valence of affective content used has been previously evaluated from a different group of individuals, as those opinions may not reflect the actual perception of our subjects; stimuli length may be too restrictive. Measured reactions to stimuli showed high variability from user to user; this can be caused also to the fact that some stimuli elicit more powerful emotions if related to previous users experiences. Interestingly in fact, talking with users after tests, we discovered that some of them perceived stimuli more relevant as they made them think about personal experiences. This factor was not taken into account and should be considered in future works. However, although some effects can been taken into account as room temperature and user position, many others are difficult to control, as comfort degree felt by subject during the test and fatigue or participant current mood. In the end we are positively oriented to continue this study, comforted from works on similar measurements and our preliminary results, also using it as a complementary measure for PNS. Future works should focus on solving problems spotted and study deeper fall rise patterns observed after stimuli.

Fig. 1 .

 1 Fig. 1. IADS sound clustering; two main regions underlined

Fig. 2 .

 2 Fig. 2. A fall rise pattern observed close after stimuli

Fig. 3 .

 3 Fig. 3. Confusion matrix for LDPM classification; percentage of correct classification, for stimuli group low (A) and high (B) impact.

European Network on Quality of Experience in Multimedia Systems and Services, COST ACTION IC1003, www.qualinet.eu