Mickaël Lallart

High gain, load-tolerant self-powered series-parallel Synchronized Switching technique for piezoelectric energy harvesting

Keywords: Energy harvesting, Piezoelectric devices, Switched circuits, Energy conversion

In the framework of autonomous wireless devices (e.g., sensors) powered by energy available from their surrounding environment, this paper exposes a technique combining the so-called series and parallel Synchronized Switch Harvesting on Inductor (SSHI) approaches for piezoelectric energy harvesting. More specifically, the proposed circuit consists in switching the piezoelectric element on two inductors, one in series between the piezoelement and the energy extraction stage (rectifier bridge) and the other one in parallel with the rectifier. This therefore allows energy transfer processes to occur several times per half period, even when the switching process is finished. Based on numerical analysis supported by experimental validations, it is shown that such a technique permits a significant widening of the effective load and rectified voltage ranges, but also, under particular conditions, a power output magnification that is even larger than in the sole SSHI case.

I. INTRODUCTION

With the exponential spreading of connected devices such as wireless sensors and sensor networks, a significant pressure on their power supply has raised. While primary batteries remain the conventional way for providing energy to these systems, their implementation and use is facing tighter and tighter constraints. More precisely, the strongest limitation of such a solution has experienced a change of paradigm with the development of ultralow-power circuits that consume very little energy. Hence, while initial concerns considered the actual energy initially stored in batteries, the main limiting factors now lie in the battery leakage, yielding a loss of energy before it is actually used ( [START_REF] Vanzwol | Designing battery packs for thermal extremes[END_REF]2]). Such an issue is even more dramatic in relatively harsh environments. For instance, under an ambient temperature of 60 ˚C, Li-Ion batteries show a drop of their energy by more than 60% over one year ( [START_REF] Vanzwol | Designing battery packs for thermal extremes[END_REF]). Hence, such a solution is not realistically applicable for devices aiming at operating on the long run in confined environments or under moderately severe conditions.

As an alternative to these battery-powered solutions, collecting energy from the direct surroundings of the device, namely M. Lallart is with the Univ. Lyon, INSA-Lyon, LGEF EA 682, F-69621, VILLEURBANNE, FRANCE (M. Lallart, LGEF INSA-Lyon, 8, rue de la Physique, 69621 VILLEURBANNE Cedex, FRANCE / e-mail: mickael.lallart@insa-lyon.fr / ORCID 0000 -0002 -6907 -5946).

energy harvesting, has been the topic of many works since two decades ( [START_REF] Shaikh | Energy harvesting in wireless sensor networks: A comprehensive review[END_REF]4]) in its modern form. Ambient sources may consider solar, thermal or vibrational energy for instance. Due to its ubiquity and constant availability, the latter is an attractive choice for microgenerators. The conversion of the mechanical energy into electricity can be achieved through electromagnetic, piezoelectric or electrostatic coupling for instance ( [START_REF] Wei | A comprehensive review on vibration energy harvesting: Modelling and realization[END_REF]). In the framework of small energy harvesters with centimeter-scale dimensions, piezoelectric coupling is of particular interest thanks to good integration potentials and relatively high power densities.

Nevertheless, when integrating the active material on the structure, and especially considering cost-effective transducers, the global electromechanical coupling at the structural level is usually limited to a few percent. Therefore, means of improving the conversion efficiency is mandatory in order to achieve sufficient output power levels. From the electrical perspective, such an achievement can be obtained thanks to a nonlinear process that consists in switching the piezoelectric transducer on an inductance each time an extremal voltage or displacement is detected. This technique, called "Synchronized Switch Harvesting on Inductor" (SSHI), allows thanks to an voltage inversion effect (permitted by the resonant circuit made of the inductor and piezoelectric capacitance during the switching event) a magnification of the piezovoltage as well as a reduced phase shift between the voltage and velocity ( [START_REF] Guyomar | Toward Energy Harvesting Using Active Materials and Conversion Improvement by Nonlinear Processing[END_REF][START_REF] Taylor | The Energy Harvesting Eel: A Small Subsurface Ocean/River Power Generator[END_REF]). These two combined effects thus contribute to an artificial increase of the electromechanical coupling, allowing a significant gain in terms of harvested power by a factor up to 10 compared to the standard approach (sole rectifier bridge). SSHI can be implemented either with the switching element either in parallel ( [START_REF] Guyomar | Toward Energy Harvesting Using Active Materials and Conversion Improvement by Nonlinear Processing[END_REF]) or in series ( [START_REF] Taylor | The Energy Harvesting Eel: A Small Subsurface Ocean/River Power Generator[END_REF][START_REF] Zhao | Series Synchronized Triple Bias-Flip Circuit: Maximizing the Usage of Single Storage Capacitor for Piezoelectric Energy Harvesting Enhancement[END_REF]). For integration purposes, the inductor can be advantageously replaced by another high frequency piezoelectric oscillator, providing kinetic energy storage by mechanical means ( [START_REF] Lallart | Inductorless Synchronized Switch Harvesting Using a Piezoelectric Oscillator[END_REF]). Furthermore, the SSHI can be made totally self-powered, thus working in a realistic fashion using a very small amount of the electrostatic energy available on the piezoelement ([11-16]). Nevertheless, in order to benefit from this power output boosting effect, the load should be carefully tuned in a similar way than in standard approach, requiring the use of additional load adaptation stages, for instance based on DC/DC converters (e.g., stepdown - [START_REF] Ottman | Optimized piezoelectric energy harvesting circuit using step-down converter in discontinuous conduction mode[END_REF] -or buck-boost - [START_REF] Lefeuvre | Buckboost converter for sensorless power optimization of piezoelectric energy harvester[END_REF][START_REF] Lallart | Low-Cost Integrable Tuning-free Converter for Piezoelectric Energy Harvesting Optimization[END_REF]), yielding further energy losses. More precisely, the series implementation of the switching element (w.r.t. the transducer and the rectifier) yields a decreased optimal load compared to the standard technique while the parallel approach increases it.

To address this load adaptation issue and still based on the switching concept, the "Synchronous Electric Charge Extraction" technique has been proposed by Lefeuvre et al. ([20,[START_REF] Wang | Multi-Input SECE based on Buck Structure for Piezoelectric Energy Harvesting[END_REF]). Contrary to the SSHI approach, the switch is reopened when the piezovoltage cancels in the SECE. This allows transferring all of the electrostatic energy available on the piezoelement to the inductance, which is latter transferred to the load and storage capacitor. Thanks to this process, the load is never directly connected to the piezoelement, hence allowing a harvested energy independent from the connected load. However, SECE shows a lower gain compared to SSHI (typically around 3 w.r.t. standard interface and taking into account losses in the energy transfer), and its self-powered implementation is much more complex ( [START_REF] Romani | Micropower Design of a Fully Autonomous Energy Harvesting Circuit for Arrays of Piezoelectric Transducers[END_REF]). While some techniques such as Double Synchronized Switch Harvesting (DSSH - [START_REF] Lallart | Double Synchronized Switch Harvesting (DSSH): A New Energy Harvesting Scheme for Efficient Energy Extraction[END_REF]), Enhanced Synchronized Switch Harvesting (ESSH - [START_REF] Shen | Enhanced synchronized switch harvesting: a new energy harvesting scheme for efficient energy extraction[END_REF]), hybrid SSHI ( [START_REF] Lallart | High Efficiency, Wide Load Bandwidth Piezoelectric Energy Scavenging by a Hybrid Nonlinear Approach[END_REF]), optimized SECE ( [START_REF] Wu | Piezoelectric vibration energy harvesting by optimized synchronous electric charge extraction[END_REF]) or SICE ( [START_REF] Lallart | Synchronous Inversion and Charge Extraction (SICE): a Hybrid Switching Interface for Efficient Vibrational Energy Harvesting[END_REF][START_REF] Xia | A Self-Powered S-SSHI and SECE Hybrid Rectifier for PE Energy Harvesters: Analysis and Experiment[END_REF]) have been proposed to combine the advantages of SSHI and SECE, they still suffer from some of the drawbacks of SSHI (load dependence) and/or SECE (limited gain, complex implementation), or can raise other issues such as integrability (e.g., due to the need of electromagnetic transformer).

Hence, the purpose of this paper is to expose a simple approach for combining, to some extent, a significant power gain (compared to maximal power of the standard approach) over a wide load range while keeping the simplicity of implementation of SSHI (self-powering abilities). The principles behind the proposed technique lie in separating the switching inductor of the SSHI into two inductors, with one placed between the piezoelectric element and the rectifier and the second in parallel with the rectifier. This therefore can be seen as a combination of series and parallel SSHI (with however some aspects from the SECE as it will be exposed in the followings), leading to the concept of "series-parallel SSHI" (sp-SSHI). The paper is organized as follows. Section II aims at exposing the basic operations of the proposed concept, which are theoretically investigated in Section III. Then, the performance of the circuit are numerically evaluated in Section IV and compared with other techniques. Finally, before a short conclusion recalling the main parts of this study, the experimental operations are evaluated in Section V, including the self-powered version assessment.

V L1 PZT SW D 1 D 2 C 1 C 2 L 1 L 2 I L1 I L2 I PZT V DC V PZT V L2 R L I h Fig. 1. sp-SSHI circuit.

II. PRINCIPLES

The concept of the series-parallel Synchronized Switch Harvesting on Inductor (sp-SSHI) lies in splitting the switching inductance, and placing one part in series with the energy extraction stage (rectifier) made of diodes D 1 and D 2 as well as smoothing capacitors C 1 and C 2 and the other part in parallel to it, resulting in the schematic depicted in Fig. 1. V P ZT , V L1 and V L2 refer to the voltage across the piezoelectric element (PZT), inductor L 1 and inductor L 2 respectively, while V DC denotes the rectified voltage across the resisitive load R L . I P ZT , I L1 and I L2 are the current flowing out the piezoelectric element and currents through inductors L 1 and L 2 respectively. Finally I h represents the current flowing through the storage stage, hence corresponding to the harvested charge flow. Note that instead of a full-wave voltage doubler, a full diode bridge rectifier may be also used, but would increase losses due to the supplementary diodes (as well as decreasing the optimal resistor). With such a configuration, current can flow from the piezoelectric element through the rectifier (series SSHI operations - [START_REF] Taylor | The Energy Harvesting Eel: A Small Subsurface Ocean/River Power Generator[END_REF]), but at the same time the parallel inductance L 2 may see its voltage clamped by half the value of the rectified one as for parallel SSHI configuration ( [START_REF] Guyomar | Toward Energy Harvesting Using Active Materials and Conversion Improvement by Nonlinear Processing[END_REF]). Additionally, there might be some residual current in the inductance L 2 after the inversion process, that can be further harvested like in the SECE approach ( [START_REF] Lefeuvre | Piezoelectric Energy Harvesting Device Optimization by Synchronous Electric Charge Extraction[END_REF]).

More precisely, the different steps in the process are exposed in Figs. 2 and3. Most of the time, the piezoelement is left in open circuit, with its voltage varying with the displacement (states a for positive voltages and f for negative ones). Once an extremal voltage occurs, the switch SW is turned on so that current is flowing from the piezomaterial to the inductance. However, in the case the voltage across the inductor L 2 is higher, in absolute value, than half the rectified voltage V DC /2 (or in other words the absolute piezovoltage is greater than (1 + L 1 /L 2 ) V DC ), the voltage across inductance L 2 is clipped to ±V DC /2 and so is the rate of the current flowing through this inductor. Consequently, part of the piezocurrent is flowing through the rectifier bridge (I h ), yielding a first step in the harvesting process (states b for switching event from positive voltage switch and g for switching event from negative voltage). When the two inductor currents, namely I L1 for L 1 and I L2 for L 2 , equal each other, the rectifier stops conducting and the piezocurrent is solely flowing through L 1 and L 2 (also yielding a change in the inversion frequency -states c and h ). However, as the current through L 2 decreases, it may eventually yield a sufficient reverse voltage so that the other part of the rectifier is conducting (states d and i ), also contributing to the harvesting process. Finally, due to the current rate limitation of L 2 , I L2 may not be zero when I L1 cancels (end of the piezovoltage inversion). Consequently, a supplementary harvesting event may go beyond the inversion process (states e and j ) before going back to state f or a .

Compared to other variants of SSHI and/or SECE and as it will be devised in the followings, the interest of the sp-SSHI lies in: techniques ( [START_REF] Lefeuvre | Piezoelectric Energy Harvesting Device Optimization by Synchronous Electric Charge Extraction[END_REF][START_REF] Wu | Piezoelectric vibration energy harvesting by optimized synchronous electric charge extraction[END_REF]) • simple implementation thanks to a single switching stage that can be easily self-powered because of switching process ending on piezoelectric current cancellation, contrary to DSSH ( [START_REF] Lallart | Double Synchronized Switch Harvesting (DSSH): A New Energy Harvesting Scheme for Efficient Energy Extraction[END_REF]), ESSH ( [START_REF] Shen | Enhanced synchronized switch harvesting: a new energy harvesting scheme for efficient energy extraction[END_REF]) or SICE ( [START_REF] Lallart | Synchronous Inversion and Charge Extraction (SICE): a Hybrid Switching Interface for Efficient Vibrational Energy Harvesting[END_REF]) for instance, which implement several switching sequences, with at least one requiring termination on maximum current that yields complex self-powered implementation • relative integrability, while hybrid SSHI (combining parallel SSHI and SSHI-MR - [START_REF] Lallart | High Efficiency, Wide Load Bandwidth Piezoelectric Energy Scavenging by a Hybrid Nonlinear Approach[END_REF]) for example requires a bulky transformer • significant power for loads range covering both series and parallel, while hybrid SSHI ( [START_REF] Lallart | High Efficiency, Wide Load Bandwidth Piezoelectric Energy Scavenging by a Hybrid Nonlinear Approach[END_REF]) is not adapted to relatively low loads values.

III. ANALYSIS

In the followings, the numerical analysis of the sp-SSHI is proposed. For the sake of simplicity and in order to concentrate on the circuit itself, it will be considered that the piezoelectric structure features constant displacement magnitude so that no damping effect will be considered. While this assumption is valid for weakly coupled and/or strongly damped devices, or when the excitation frequency is sufficiently far away from the resonance frequency, it is however possible to take into account the damping effect induced by the harvesting process by using Mason electromechanical model ( [START_REF] Mason | Electromechnical Transducers and Wave Filters[END_REF]) for instance. Additionally, it will be considered that the smoothing capacitors C 1 and C 2 are large enough so that the time constant defined by these capacitors and the load R L is much larger than the vibration period, yielding an almost constant value of the rectified voltage V DC .

Under the considered hypotheses, only the piezoelectric electrical equation can be considered. Using a lumped model representation ( [START_REF] Badel | Finite Element and Simple Lumped Modeling for Flexural Nonlinear Semi-passive Damping[END_REF]), this turns to a velocity controlled current source with a capacitive internal impedance as:

I P ZT (t) = α u(t) -C 0 VP ZT (t) (1)
where I P ZT (t) and V P ZT (t) are the outgoing piezocurrent and piezovoltage respectively, u(t) the structure flexural displacement, and α and C 0 the force factor and clamped capacitance, respectively.

A. State equations

For setting up the numerical description of the sp-SSHI operations, the state vector Y(t) made of the piezoelectric voltage V P ZT (t), and the currents flowing through inductors L 1 and L 2 , namely I L1 (t) and I L2 (t) respectively, will be considered:

Y(t) =   V P ZT (t) I L1 (t) I L2 (t)   (2)
while the input vector X(t) is dependent on the structure displacement u(t) and rectified voltage V DC (recalling the latter is assumed constant):

X(t) = u(t) V DC (3)
Based on this model, the derivative of state vector Y, denoted Ẏ can be written as:

Ẏ(t) = A x Y(t) + B x X(t) (4)
with A x and B x the respective 3 × 3 and 3 × 2 state matrices, where the subscript x denotes the state ( a to j as defined in Fig. 3).

In non-switching state and without conduction (states a and f ), no current is flowing in the whole circuit (except from the storage capacitor to the load, which is not to be taken into account here as the rectified voltage V DC is assumed constant). Therefore, the expression of the associated state matrices leads to the following expressions:

A a = A f =   0 0 0 0 0 0 0 0 0   B a = B f =   α C0 0 0 0 0 0   (5)
Once an extremum value is reached, the switch SW is closed. Therefore a current is flowing from the piezoelement to the inductors. However, the current rate through L 2 is limited due to the conduction of the rectifier bridge, that clips the voltage across L 2 to V DC /2 for switching from positive voltage (state b ) or -V DC /2 when the switching starts from a negative voltage (state g ). This yields the following definition of the state matrices:

A b = A g =   0 -1 C0 0 1 L1 -r1 L1 0 0 0 -r2 L2   B b =   α C0 0 0 -1 2L1 0 1 2L2   ; B g =   α C0 0 0 1 2L1 0 -1 2L2   (6)
Note that in these expressions, losses in the inductors have been taken into account and modeled by pure resistances (r 1 and r 2 , respectively for inductors L 1 and L 2 ).

As the current through L 1 experiences a decreasing rate, it eventually equals those of L 2 . In that case, the absolute voltage across L 2 becomes less than V DC /2 and the rectifier bridge is blocked. This corresponds to states c and h , where the same current flows through L 1 and L 2 . The corresponding states matrices are then given by:

A c = A h =   0 -1 C0 0 1 L1+L2 -r1+r2 L1+L2 0 1 L1+L2 -r1+r2 L1+L2 0   B c = B h =   α C0 0 0 0 0 0   (7)
It can be noted that in that case, the instantaneous natural inversion frequency changes from 1/ 2π √ L 1 C 0 to 1/ 2π (L 1 + L 2 ) C 0 (not taking into account the effect of losses), as the voltage across L 2 is no longer clipped by the rectifier voltage.

As the current decreases however, its absolute rate reaches once again the maximum rate allowed through L 2 (voltage clipping to ∓V DC -states d and i ). In that case, the state matrices yield:

A d = A i =   0 -1 C0 0 1 L1 -r1 L1 0 0 0 -r2 L2   B d =   α C0 0 0 1 2L1 0 -1 2L2   ; B i =   α C0 0 0 -1 2L1 0 1 2L2   (8)
Finally, as the piezovoltage inversion stops (cancellation of the current through L 1 ), residual current may still flow through L 2 and the rectification stage (states e and j ). The associated state matrices are given by:

A e = A j =   0 0 0 0 0 0 0 0 -r2 L2   B e =   α C0 0 0 0 0 -1 2L2   ; B j =   α C0 0 0 0 0 1 2L2   (9)

B. State switching conditions and derivation of other quantities

While the previous analysis allowed to derived the governing equation for each state, this subsection discusses the conditions for changing from one state to another one. To this end, the voltage V L2 across inductor L 2 is also considered:

V L2 = L 2 İL2 + r 2 I L2 (10)
Additionally, the previously exposed model being intended to be solved through numerical iterations, the variables are here considered as discrete. However, for the sake of clarity, the discrete step index will be omitted.

From the open circuit stage a (resp. f ), the condition to enter the switching state b (resp. g ) is either based on the velocity or the voltage. The SSHI is indeed based on connecting the switching circuit on velocity cancellation ( u = 0), but practically, its implementation relies, in a equivalent way, on the detection of extremal voltage.

Once in state b (resp. g ), where the voltage across L 2 is clipped by +V DC /2 (resp. -V DC /2) and with current from the piezoelectric element flowing through L 1 , the absolute current increase rate through L 2 is thus limited and is less than the absolute current through L 1 (Fig. 2). The end of this state is not only determined by V L2 , but also considering the current flowing through the diodes D 1 and D 2 which should be direct. Hence, the following conditions should be satisfied to stay in state b (resp. g ):

|V L2 | ≥ V DC 2 sgn (V L2 ) × (I L1 -I L2 ) ≥ 0 (11)
with sgn(x) the signum function of the variable x. In Eq. ( 11), the second condition relates the current direction in the diodes (the signum of V L2 allowing determining which diode conducts, while the current differentiation gives the current direction). When one of these conditions is no longer met, the circuit thus enters in state c (resp. h ).

When operating in state c (resp. h ), the rectification stage is blocked, and the inversion is solely done through L 1 and L 2 (the inversion frequency is thus reduced compared to states b , d , i and g ). The condition to stay in this state c (resp. h ) is to have a voltage across L 2 , V L2 , higher than -V DC /2 (resp. lower than +V DC /2):

|V L2 | ≤ V DC 2 (12)
As soon as this condition is no longer satisfied, the circuit enters in state d (resp. i ), where the inversion is performed with one of the rectifier branch conducting. Similarly to state b (resp. g ), the condition to stay in this configuration is given by Eq. ( 11).

Once Eq. ( 11) is no longer valid, the piezovoltage inversion process is terminated but residual current in L 2 yields a continuation of the diode bridge conduction (state eresp. j ). This conduction lasts until the absolute voltage across L 2 , namely |V L2 |, is greater than V DC /2 and as a direct current flows through D 2 (resp. D 1 ), yielding same conditions as in Eq. ( 11). Once terminated, the circuit goes back to original configuration (state fresp. a ).

IV. NUMERICAL DISCUSSION

This section proposes to discuss the performance of the sp-SSHI architecture under various parameter configurations. Assuming the piezoelectric element driven by a monochromatic signal of frequency f , harvested energy for one switching cycle W harv can be obtained through one of the following relationships (respectively referring to positive and negative piezovoltage switching):

   W harv = V DC 2 te| end t b | beg |I h (t)| dt W harv = V DC 2 tj | end tg| beg |I h (t)| dt (13)
where t x | beg refers to the time instant when state x begins and t y | end the time instant when the state y consecutive to x finishes. I h (t) is besides defined as the current flowing through the harvesting stage:

I h (t) = I L1 (t) -I L2 (t) (14)
As two harvesting processes occur per period (respectively on positive and negative voltage values), the total harvested power P harv turns to:

P harv = 2f W harv (15)
For comparison purposes, other typical circuits consisting of standard interface (sole rectifier bridge with smoothing capacitor [START_REF] Ottman | Adaptive piezoelectric energy harvesting circuit for wireless remote power supply[END_REF]), parallel SSHI (rectifier with smoothing capacitor and switching unit in parallel to it [START_REF] Lefeuvre | A comparison between several vibration-powered piezoelectric generators for standalone systems[END_REF][START_REF] Guyomar | Toward Energy Harvesting Using Active Materials and Conversion Improvement by Nonlinear Processing[END_REF]) and series SSHI (rectifier with smoothing capacitor and switching unit in series between the piezoelectric element and rectifier [START_REF] Lefeuvre | A comparison between several vibration-powered piezoelectric generators for standalone systems[END_REF][START_REF] Taylor | The Energy Harvesting Eel: A Small Subsurface Ocean/River Power Generator[END_REF]) may be considered. The associated power expressions, in the case of full-wave voltage doublers, are given by:

P stand = f V DC (2αu M -C 0 V DC ) P p-SSHI = f V DC 2αu M -1-γ 2 C 0 V DC P s-SSHI = 1+γ 1-γ f V DC (2αu M -C 0 V DC ) (16)
where γ denotes the voltage inversion factor:

γ = e -π 2Q i , Q i = 1 r L C 0 (17)
with L and r the respective inductor value and equivalent loss resistor in the switching unit (including inductor losses).

Obtained results in terms of output voltages and powers are depicted in Fig. 4 for several cases of adimensional inductance values defined as:

Lx = L x C 0 (2πf ) 2 ( 18
)
with χ is the adimensional counterpart of the parameter χ, and x is the subscript associated to the parameter1 . Also, constant electrical quality factor Q i has been considered for the inductors, which has been potentially varied from one simulation to another. In practical application however, and because some parts of the switching event would experience larger inversion frequency as L 1 decreases (keeping L 1 + L 2 constant), the electrical quality factor may slightly increase and so would be the power. Hence, presented results may be slightly underestimated. Load values have been obtained by dividing the squared rectified voltage by the calculated associated power output. Finally, for an easier and general comparison, the powers, voltages and loads have been normalized to the respective maximal power, optimal voltage and optimal load in the standard case. These results demonstrates that, for relatively high L 2 /L 1 inductance ratio and moderate to high electrical quality factor, a significant harvested power can be achieved over a wide voltage/load range, combining the advantage of SSHI in terms of power gains and SECE in terms of power robustness facing load variations. However, the maximal voltage that can be achieved in quasi open-circuit keeps lower than the parallel SSHI. Under the considered conditions (constant electrical quality factor for each plot), it can be noticed that the total inductance value has almost no impact on the power curves, while the inductance ratio plays a great role. While all the curves starts following the response of series SSHI, increasing the load (or rectified voltage) may induce a second peak in terms of power. Especially, for relatively high value of inductance L 2 (hence with significant energy stored in this inductance), the second peak is getting closer to the parallel SSHI optimal point as L 2 value increases. This therefore permits a wide load range with a significant output power, although a relatively limited well appears as the electrical quality factor (or equivalently the inversion factor) increases. In order to evaluate the electrostatic energy available on the piezoelectric energy as well as the conversion enhancement enabled by the switching process, Fig. 5 depicts the voltage just before the energy extraction. Hence, it can be noticed that the latter is particularly high for low load values in the case of series SSHI, and for high load values for parallel SSHI. This is explained by the fact that the voltage inversion is done with respect to half the rectified voltage in the former case, and by the voltage clipping to half the rectified voltage in the latter approach. Hence, this may yield significant losses, especially related to diode threshold voltage, when the load is high for the series SSHI (as almost no inversion event occurs) and for low load values in the case of parallel SSHI (the voltage clipping being mostly induced by the diodes). In the case of sp-SSHI however, thanks to the hybrid approach enabled by the two inductors, it can be noticed that the piezovoltage remains at a rather high value over the full load range. Hence, this not only denotes the better ability for enhancing the energy conversion, but also permits to mitigate the conduction losses during charge transfer to the storage stage. Also, thanks to the supplementary energy stored in L 2 that is then released as the inversion event is terminated, natural discharge to the storage stage happens, with an overvoltage peak that allows bypassing the diode thresholds.

The inductance values also impact the inversion time and thus required timing and associated power consumption. Additionally, contrary to previous SSHI and SECE approaches, changing the rectified voltage, or equivalently the load, yields a modification of the inversion time (steps b to d and g to i in Fig. 2). More precisely, the voltage being clipped in steps b , d , g and i , only inductor L 1 contributes to the inversion time, while both inductors intervene in steps c and h . Hence, the total inversion time t i is comprised between the two boundaries defined by the total effective inductor as:

π L 1 C 0 ≤ t i ≤ π (L 1 + L 2 ) C 0 (19)
where the lower boundary corresponds to low voltages/loads (piezovoltage almost always clipped) and the upper one to large values of voltage or load (almost no clipping). Hence, reducing the inductance values may allow reduced inversion time, which however can significantly increase the losses, especially due to the higher current peak caused by faster switching process (notably due to nonlinear effects such as iron losses). These losses besides vary with the load or voltage which change the inversion time and peak current. Nonetheless, typical implementations of self-powered switches ([11-13]) rely on a diode for stopping the inversion (which occurs when the current flowing out the piezoelement cancels). Therefore, even if the inversion time may vary, this allows an automatic adjustment of the switching time.

Another very interesting feature of the sp-SSHI is its ability to provide an outstanding power boost at low voltage/load values, even higher than the conventional parallel or series SSHI for a significant value of the electrical quality factor. Such a unique feature is explained by the additional energy stored in inductance L 2 , especially at low rectified voltage/load. Indeed, while the voltage clipping yields charges flowing to the storage stage (as in the series SSHI case), some extra energy is also accumulated by L 2 , and then released to the storage in the next steps (e.g., states e and j ). This energy therefore adds up to the former one, thus leading to a substantial gain compared to the traditional SSHI approaches. As the rectified voltage/load are increasing, the structure gets closer to the parallel SSHI still through L 2 , and most of the current flowing through this inductance is used for voltage flipping (reduced voltage clipping).

V. EXPERIMENTAL VALIDATION

Based on the previous concept and analysis, this section aims at experimentally assessing the real performance of the proposed circuit. In order to reflect as much as possible realistic implementation, the device will be made totally autonomous through the use of a self-powered switching unit.

A. Experimental set-up

The experimental device (Fig. 6) is made of a piezoelectric cantilever beam obtained by cutting two commercial buzzers (RS PRO 837-7844). The two buzzer pieces were then bonded onto each other through their substrate. The output terminal of each beam have been connected in serial configuration. This electromechanical structure is mounted on an electrodynamic shaker fed by a function generator through a power amplifier that applies a base acceleration on the piezoelectric system. The excitation frequency is set to 100 Hz which is slightly off resonance, and the tip displacement magnitude is kept constant to 120 µm peak and monitored using a laser optical sensor. Electrical signals (piezovoltage, function generator output voltage and displacement sensor output) are observed through an oscilloscope while DC output voltage is monitored using a multimeter.

The self-powered switch is implemented following the initial proposal made by Richard et al. ([11,[START_REF] Lallart | Optimized Self-Powered Switching Circuit for Non-Linear Energy Harvesting with Low Voltage Output[END_REF]), as depicted in Fig. 7. While it is less compact than the configuration proposed by Eltamaly and Addoweesh in [START_REF] Eltamaly | A Novel Self-Power SSHI Circuit for Piezoelectric Energy Harvester[END_REF] which advantageously uses some transistors for two functions (diode for filtering during one half period, comparator for the other half), it allows better handling high voltages. 5 inductors (RL181S-104J-RC, manufactured by Bourns), each with a value of 100 mH, have been connected serially, with access to each intermediate node to connect the harvesting stage in the sp-SSHI configuration. For series and parallel SSHI, all of the 5 inductors were kept in the inversion circuit for fair comparison. Two 47 µF/25 V capacitors, picked from CCC-14 kit from Nova, were selected as smoothing capacitors. The output of the harvesting stage was then connected to a varying resistive load. In the case of loads greater than 1 MΩ, the voltage was monitored on a series 1 MΩ resistor (that was taken into account in the total load value) in order to ensure a negligible effect of the voltmeter input impedance (typically 10 MΩ), and the total rectifier voltage was derived using standard resistive divider formula.

Prior to power measurements, model identification has been performed. Obtained parameters are listed in Table I. The inversion factor γ was obtained with the harvesting stage disconnected, allowing the derivation of the electrical quality factor according to Eq. ( 17).

B. Result and discussions

Based on the previously exposed experimental device and set-up, energy harvesting capabilities were assessed. The influence of the parallel to series inductance ratio was considered, while keeping the total inductance constant (including in the SSHI cases). Results are exposed in Fig. 8.

These results therefore confirm the capabilities of the sp-SSHI for providing similar performance than the series and parallel SSHI in terms of maximal output power, while allowing a wide range of operating loads (or equivalently, of states of the connected electronic device). While the series and parallel SSHI half-power load range are roughly [10 kΩ, 300 kΩ] and [100 kΩ, 4 MΩ] respectively, it is gaining a factor of 5 when using the sp-SSHI ([10 kΩ, 2MΩ]), hence covering more than two decades. It is also noteworthy that experimentally obtained results in terms of power for the sp-SSHI are better than in the theoretical case, which may be explained by higher electrical quality factor as the switching frequency increases when the voltage across L 2 is clipped.

Such an effect actually leads to two voltage peaks that appear even for relatively low values of the electrical quality factor. Fig. 9 shows the voltage waveforms obtained in the case of L 2 =400 mH and L 1 =100 mH for the two power peaks. Hence, it can be noticed that for the lowest optimal load (50 kΩ), three harvesting events occur (voltage clipping for each polarity, with the second one continuing after the switching event) with an almost instantaneous change from one harvesting event to the other one, while for the highest optimal load (200 kΩ), almost only one harvesting event arises.

VI. CONCLUSION

This study introduced an architecture aiming at improving the energy harvesting performance of piezoelectric microgenerator. By splitting the inductance used in the SSHI approach, and connecting the harvesting stage (rectifier) at the middle point, a hybrid interface between series and parallel configuration is achieved. One particular aspect of such an approach is that the inductance in parallel with the rectification stage permits a supplementary energy release step after the switching event. The performance analysis of the circuit, based on state-space simulations and experimental investigations, demonstrated that a wider range of operating voltage or resistive load is achieved. Additionally, under particular conditions (limited losses and high ratio of the parallel inductance over the series one), the proposed techniques permits and outstanding gain, even bypassing the case of parallel SSHI. Further works may consider the investigation of the damping induced by such a technique, as well as integrated solutions limiting the losses in the energy transfer steps and inversion events.

Table II proposes as a general synthesis a comparison between the sp-SSHI technique and other typical approaches. Hence, it can be observed that the proposed method permits a high relative power while ensuring a relatively important load bandwidth and simple and rather compact self-powered implementation. This therefore provides a very attractive means of harvesting energy.

Fig. 2 .Fig. 3 .

 23 Fig. 2. sp-SSHI typical waveforms.

Fig. 4 .

 4 Fig. 4. Obtained voltages and powers from numerical resolution.

Fig. 5 .

 5 Fig. 5. Piezoelectric element voltage just before switching.

Fig. 6 .

 6 Fig. 6. Experimental set-up.

Fig. 8 .

 8 Fig. 8. Experimental voltages and harvested powers and comparison with numerical and theoretical resolutions.

Fig. 9 .

 9 Fig. 9. Waveforms for L 1 =100 mH and L 2 =400 mH with 50 kΩ and 200 kΩ loads.

Note that thanks to the switching principles, the normalized inductance value is much less than the unit (the latter corresponding to passive impedance matching), denoting a more realistic implementation.

VII. ACKNOWLEDGEMENTS

The authors acknowledge the support of Institut Carnot Ingénierie@Lyon for partly funding this research through the IMPACT project.


Saheb Chhabra

Naman Banati

Gaurav Gupta

Garima Gupta

Chapter 13 TARGET IDENTITY ATTACKS ON FACIAL RECOGNITION SYSTEMS

Keywords: Counterfeiting, facial recognition, target identity attacks

Advancements in digital technology have significantly increased the number of cases involving the counterfeiting of identity documents. One example is exam fraud, where a counterfeiter creates a composite morphed photograph of the real candidate and an imposter, and attaches it to the examination admit card. Automated facial recognition systems are beginning to be deployed at examination centers to match candidates' faces against their official facial images. While the need to perform manual matches is eliminated, the vulnerabilities of these automated systems are a major concern.

This chapter evaluates the vulnerability of an automated facial recognition system to input image manipulation via a target identity attack. The attack manipulates a facial image so that it looks similar to the real candidate, but outputs the identity feature representation of the imposter. This chapter also evaluates the performance of facial recognition models with regard to impersonator recognition. Experiments using image databases demonstrate the effectiveness of target identity attacks.

Introduction

Counterfeiting of identity documents is one of the fastest-growing frauds worldwide. Advancements in digital technology enable counterfeiters to perpetrate sophisticated frauds by creating fake identity cards and other related documents. An example is exam fraud, where a counterfeiter morphs the photograph of a real candidate with that of an imposter, and creates a tampered examination admit card with the morphed photograph. The quality of the counterfeit admit card makes it very difficult for a human examiner to determine that the person who presents the card at an examination center is an imposter. The imposter then proceeds to take the exam on behalf of the real candidate.

Several instances of exam fraud involving the manipulation of facial images in admit cards have been reported by the international media. In 2018, law enforcement officers in Jodhpur, India arrested several members of a gang involved in a police examination cheating case [START_REF]11 members of police exam cheating gang arrested in Jodhpur, The Pink City Post[END_REF]. The gang employed a team of 20 subject matter experts to take entry examinations on behalf of candidates who paid large sums of money. In another case [START_REF] Yuhas | Chinese nationals charged with cheating by impersonation on US college tests[END_REF], fifteen Chinese nationals were arrested for using counterfeit Chinese passports to take U.S. college entry tests such as the SAT, GRE and TOEFL.

Automated facial recognition systems are beginning to be deployed at examination centers to match candidates' faces against their official facial images. These systems perform a two-step verification procedure. The first step matches the facial image provided at the time of exam registration against the facial image of the supposed candidate who shows up at the examination center. The second step matches the photograph on the admit card against the facial image of the candidate that was provided at the time of exam registration.

Figure 1 shows how an automated facial recognition system identifies an imposter by comparing the photograph on the presented admit card against the facial image of the candidate that was provided at the time of exam registration.

The security of automated facial recognition systems is a major concern. Therefore, this research focuses on the vulnerability of an automated system to image manipulation using a novel target identity attack. This attack introduces perturbations in the facial image of the real candidate such that the manipulated image appears to be of the real candidate, but it outputs the identity features of an imposter (target). This manipulated image is submitted at the time of candidate registration. Thus, the imposter is able to masquerade as the real candidate and take the examination on his or her behalf. Figure 2 demonstrates a successful target identity attack. This chapter focuses on the vulnerability of automated facial recognition systems to target identity attacks. It also evaluates the performance of facial recognition models with regard to impersonator recognition. Experiments using image databases demonstrate the effectiveness of target identity attacks.

Related Work

Several researchers have proposed morphing techniques that target facial recognition and other biometric systems [START_REF] Scherhag | Face recognition systems under morphing attacks: A survey[END_REF]. Korshunova et al. [START_REF] Korshunova | Fast face-swap using convolutional neural networks[END_REF] have proposed a morphing technique similar to style-transfer-based deep neural networks using a novel loss function. Othman and Ross [START_REF] Othman | Privacy of facial soft biometrics: Suppressing gender but retaining identity[END_REF] have used a morphing method to fool gender classifiers. The method morphs an input image with an image of a subject of the opposite gender; two parameters are used to control the appearance of the final image with gender suppression information. Mirjalili et al. [START_REF] Mirjalili | Gender privacy: An ensemble of semi adversarial networks for confounding arbitrary gender classifiers[END_REF] have extended the work of Othman and Ross so that the input candidate image is fused with another candidate image selected based on a correlation between facial landmark points. Delaunay triangulation is employed to identify the pixels to be modified.

Damer et al. [START_REF] Damer | MorGAN: Recognition vulnerability and attack detectability of face morphing attacks created by generative adversarial networks[END_REF] have proposed a generative adversarial network (GAN) method that employs representation loss to create morphed images. Ferrara et al. [START_REF] Ferrara | On the feasibility of creating double-identity fingerprints[END_REF] have proposed a method that creates double identity fingerprints; the features of two fingers are combined to yield a new fingerprint that fools fingerprint recognition systems. Rathgeb and Busch [START_REF] Rathgeb | On the feasibility of creating morphed iris codes[END_REF] have developed a stability-based bit substitution method that morphs two iris codes.

Several researchers have proposed methods for detecting morphed images. Raghavendra et al. [START_REF] Raghavendra | Transferable deep-CNN features for detecting digital and print-scanned morphed face images[END_REF] have proposed an approach that employs fine-tuned deep convolutional neural networks and a probabilistic collaborative representation classifier (P-CRC). The approach extracts features from fully-connected layers of VGG19 [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] and AlexNet [START_REF] Krizhevsky | ImageNet classification with deep convolutional neural networks[END_REF] models, and concatenates them before sending them to the classifier. Seibold et al. [START_REF] Seibold | Detection of face morphing attacks by deep learning[END_REF] compare the morphing detection performance of pre-trained and trained-from-scratch AlexNet, GoogLeNet and VGG19 models. They created a database using the triangle and mesh warping techniques.

Wandzik et al. [START_REF] Wandzik | Morphing detection using a general purpose face recognition system[END_REF] have proposed a technique for distinguishing between original and morphed images. The technique employs features extracted from four facial recognition models and classifies them using a support vector machine (linear classifier). Batskos et al. [START_REF] Batskos | Face morphing detection[END_REF] have developed a distance-based approach for detecting morphed images. Euclidean distances are computed for probe and morphed, morphed and e-pass, and probe and e-pass facial features, which yield 3D vectors. A support vector machine is used for linear classification.

Zhang et al. [START_REF] Zhang | Face morphing detection using the Fourier spectrum of sensor pattern noise[END_REF] have proposed a source identification scheme for detecting whether an image is bona fide or morphed. Scherhag et al. [START_REF] Scherhag | Detecting morphed face images using facial landmarks[END_REF] have developed a technique for distinguishing morphed images using facial landmark points; the technique assumes that the intra-subject variance of landmarks extracted from bona fide images is less than the variance between landmarks of the morphed image and its contributing subjects. Debiasi et al. [START_REF] Debiasi | PRNU variance analysis for morphed face image detection[END_REF] have proposed a photo response non uniformity (PRNU) approach for detecting morphed images. Their approach assumes that the variance of PRNU signals increases across image cells when two images are morphed to create a single image.

Damer et al. [START_REF] Damer | Detecting face morphing attacks by analyzing the directed distances of facial landmark shifts[END_REF] have employed two scenarios for morphing detection (i.e., with and without a probe). In the first scenario, facial landmarks are determined using an ensemble of regression trees, explicit shape regression and regressing local binary features; Euclidean distances are computed between the landmarks of live and previously-submitted images. In the second scenario, facial features are extracted using local binary pattern histograms and transferable deep convolutional neural networks.

Makrushin et al. [START_REF] Makrushin | Automatic generation and detection of visually faultless facial morphs[END_REF] have proposed a morph detection algorithm, which assumes that some blocks in morphed images have undergone JPEG compression; however, blocks are not compressed in newly morphed images. The algorithm performs JPEG compression on a morphed image and determines nine Benford features that are used for classification. Finally, Neubert et al. [START_REF] Neubert | A face morphing detection concept with a frequency and spatial domain feature space for images on eMRTD[END_REF] have employed frequency and spatial domain features for morph detection.

The review of the literature reveals that morphing methods have limitations that introduce ghosting artifacts and change the visual appearances. Figure 3 shows the images obtained using four morphing methods (from left to right): (i) Neubert et al. [START_REF] Neubert | A face morphing detection concept with a frequency and spatial domain feature space for images on eMRTD[END_REF]; (ii) Batskos et al. [START_REF] Batskos | Face morphing detection[END_REF]; (iii) Scherhag et al. [START_REF] Scherhag | Detecting morphed face images using facial landmarks[END_REF]; and (iv) Damer et al. [START_REF] Damer | Detecting face morphing attacks by analyzing the directed distances of facial landmark shifts[END_REF]. Note that the first and second rows show the original images whereas the third row shows the morphed composite images.

Target Identity Attacks

A target identity attack introduces adversarial perturbations in the facial image of the real candidate (source image) such that the manipulated image appears to be similar to the real candidate, but it outputs the identity features of the facial image of the imposter (target image). The manipulated image is submitted at the time of candidate registra- tion. Thus, the imposter can fool an automated facial recognition system and masquerade as the real candidate.

Figure 4 shows a block diagram of a target identity attack. The adversarial perturbation N is initialized as zero in the first iteration. In subsequent iterations, the perturbed image obtained by adding perturbations N to the source image S, and the target image T is provided an input to the facial recognition model. Optimization is performed over perturbations N until the stopping criterion is satisfied.

The detailed steps involved in the target identity attack are discussed below. In the following, the terms real candidate image and source candidate image are used interchangeably. Likewise, the imposter candidate image and target candidate image are used interchangeably.

The fundamental problem is to manipulate a source candidate image so that the manipulated image outputs the identity features of the target candidate image while appearing to be similar to the source candidate image.

Let S be the source candidate image and T be the target candidate image in the range [0, 1]. Let P be the manipulated or perturbed image generated by adding perturbation N to the source candidate image S. In order for the perturbed image to be valid, it should be in the range [0, 1]. Mathematically, this is written as: where P ∈ [0, 1].

To satisfy this constraint, the following transformation is used to generate a perturbed image P in the range [0, 1]:

P = 1 2 (tanh(S + N) + 1) (2)
Let V and I denote the visual appearance and identity of an image, respectively. The goal is to generate a perturbed image P such that its visual appearance is similar to the source candidate image S and its identity representation is similar to the target candidate T. This is mathematically formulated as:

V P = V S ; I P = I T (3)
where V P and V S are the visual appearances of the perturbed image P and source candidate image S, respectively; and I P and I T are the identities of the perturbed image P and source candidate image S, respectively. Two loss functions f (P) V and f (P) I are specified to incorporate the constraints mentioned above in the attack. The first loss function f (P) V deals with the visual appearance of the perturbed image and the second loss function f (P) I deals with the identity representation of the perturbed image. Both the functions have to be minimized as follows:

Min{f (P) V + f (P) I } (4)
In order to make the visual appearance of the perturbed image P similar to the source candidate image S, the distance between S and P must be minimized. Thus, the function f (P) V is written as:

f (P) V = D(P, S) ( 5 )
where D is the distance metric.

Since the Euclidean distance is used as the metric, Equation ( 5) is written as:

f (P) V = ||P -S|| 2 (6)
The next task is to make the identity representation of the perturbed image P similar to the identity representation of the target image T.

Let φ be a pre-trained facial recognition model with weights W and bias b. This model takes an image as input and outputs its identity representation. Therefore, the identity representation R T of an input target candidate image T is computed as:

R T = φ(WT + b) ( 7 )
The corresponding identity representation R P of the perturbed image P is computed as:

R P = φ(WP + b) ( 8 )
The next task is make the identity representation of the perturbed image P similar to the identity representation of the target candidate image T. The distance between the identity representation of the perturbed image P and the target candidate image T is minimized. Thus, the function f (P) I is written as:

f (P) I = D(R P , R T ) ( 9 )
The Euclidean and cosine distance metrics are used to minimize the distance between the identity representations of the perturbed image P and target candidate image T. Thus, the overall objective function is written as:

Min{||P -S|| 2 + D(R P , R T )} ( 10
)
which is optimized over the perturbation variable N.

Experiments and Results

Experiments were performed to evaluate the effectiveness of target identity attacks. One set of experiments was performed under two scenarios, one involving white-box attacks and the other involving black-box attacks. Another set of experiments, involving impersonator recognition using pre-trained models, was performed to evaluate the performance of pre-trained facial recognition models in impersonator recognition.

The first set of experiments was performed on the Labeled Faces in the Wild (LFW) dataset [START_REF] Huang | Labeled faces in the wild: A database for studying face recognition in unconstrained environments[END_REF]. The second set of experiments was performed on the Disguised Faces in the Wild (DFW) dataset [START_REF] Kushwaha | Disguised faces in the wild[END_REF].

The following dataset details and evaluation protocols are pertinent:

Labeled Faces in the Wild (LFW) Dataset: This dataset contains 13,233 facial images of 5,749 subjects. The evaluation of target identity attacks in the white-box and black-box scenarios employed View 2 of the LFW dataset, which comprises 6,000 pairs of images. Of the 6,000 pairs of images, 3,000 pairs are genuine images while the remaining 3,000 pairs are imposter images.

The target identity attacks were performed by perturbing one image from each imposter pair so that its identity representation becomes similar to the other image in the imposter pair. The DFW dataset provides three protocols. This research employed Protocol 1 (impersonation) to evaluate facial recognition models. Specifically, Protocol 1 is used to distinguish impersonators from legitimate subjects. In the protocol, the combination of a normal image with a validation image of the same subject corresponds to a genuine pair. The combination of an impersonator image with the normal, validation and disguised images of the same subject corresponds to an imposter pair.

Table 1 provides details about the two sets of experiments. The first set of experiments employed the VGGFace facial recognition model (pretrained with the VGGFace dataset) and the ResNet50 facial recognition model (pre-trained with the VGGFace2 dataset); target identity attacks on the two facial recognition models were evaluated using the LFW dataset. The second set of experiments employed the VGGFace, ResNet50 and LCNN-29 facial recognition models for impostor recognition; the DFW dataset was used in the evaluation.

Implementation Details

The experiments were performed on an NVIDIA Tesla P100 server with 96 GB RAM and 16 GB GPU memory. All the images were resized to 224 × 224 pixels.

The target identity attacks were performed by learning the perturbation corresponding to each image to be attacked. The attacks were implemented in Tensorflow v1.9.0. The learning rate was set to 0.1 during the training phase. The perturbations were adjusted over 15 iterations.

Attack Performance Evaluation

This section discusses the performance of target identity attacks. The Euclidean distance and cosine distance were used as performance metrics.

Target Identity Attacks. This set of experiments evaluated a scenario where perturbed images are presented in place of real candidate images during the exam registration process. The perturbed image would look similar to the real candidate image, but it would output the imposter or target identity representation.

In a real-world scenario, the counterfeiter would not know the facial recognition model that is used to authenticate supposed candidates. Therefore, the target identity attacks are evaluated for white-box and black-box facial recognition scenarios. In a white-box facial recognition scenario, the counterfeiter knows the facial recognition model used to authenticate candidates and generates perturbed images corresponding to the same facial recognition model. In a black-box facial recognition scenario, the counterfeiter does not know the facial recognition model used to authenticate candidates and, therefore, generates perturbed images corresponding to a different facial recognition model.

As mentioned above, 3,000 imposter pairs were considered when implementing the target identity attacks. One image in each pair was perturbed to obtain an identity representation similar to that of the other image (target identity) in the pair. To evaluate the performance, the distance between the target identity representation and the identity representation of the perturbed image was computed for each pair. In the ideal case, this distance should be zero for a successful attack.

Table 2 shows the mean distance scores for 3,000 imposter pairs before and after performing the target identify attacks. Note that good results are obtained for the white-box and black-box scenarios. For example, when perturbed images are generated for the VGGFace model and evaluated using the same model and the cosine distance metric, the mean distance is reduced by 0.34 (= 0.42 -0.08). Similarly, when perturbed images are generated for the VGGFace model and evaluated using the ResNet50 model and the Euclidean metric, the mean distance score is reduced by 0.14 (= 1.20 -1.06).

Figure 5 compares the imposter distance score distributions for 3,000 imposter pairs before and after target identity attacks in the whitebox and black-box scenarios. Figure 5(a) shows the score distributions obtained using the VGGFace and ResNet50 models when the images were perturbed based on the ResNet50 model. Figure 5(b) shows the score distributions obtained using the VGGFace and ResNet50 models when the images were perturbed based on the VGGFace model.

Figure 5(a) and 5(b) consistently show that the distributions are shifted towards the left or to zero after the target identity attacks. This demonstrates that the target identity attacks are effective.

Figures 6 and7 compare the genuine and imposter distance score distributions before and after identity target attacks in the white-box and black-box scenarios. The images in Figure 6 are perturbed based on the VGGFace model whereas the images in Figure 7 are perturbed based on the ResNet50 model.

Figures 6 and7 consistently show that the imposter distance score distributions are shifted closer towards the genuine distance score distributions in the black-box scenario. Moreover, the overlaps between the genuine and imposter distance score distributions are increased. These results demonstrate that the target attacks are effective at fooling the facial recognition models.

Figure 8 shows three sets of images generated via morphing and via target identity attacks. The first and second columns in the figure show the images of two different people, denoted as identity A and identity B, respectively. The third column shows the images generated via morphing whereas the fourth column shows the images generated via target image attacks.

In the case of the morphed images, the visual appearances and the identities correspond to both A and B. However, in the case of the target identity attack images, the visual appearances correspond to A whereas the identities correspond to B. Clearly, the target identity attack images in the fourth column preserve the original A appearances to a greater degree than the morphed images in the third column. However, because their identities correspond to B, the target identity attack images could be used to successfully perpetrate exam fraud. Impersonator Recognition with Pre-Trained Models. This set of experiments evaluated a scenario where an impersonator uses a target identity attack image to take an examination on behalf the real candidate. The target identity attack image would be submitted to the authorities when the real candidate registers for the examination. By fooling the automated facial recognition system, the imposter would be able to masquerade as the real candidate and take the examination on behalf of the candidate. Figure 9 shows the receiver operating characteristic (ROC) curves obtained using pre-trained VGGFace, ResNet50 and LCNN-29 facial recognition models with the Euclidean and cosine distance metrics. The experiments used the DFW dataset and Protocol 1. The ROC curves demonstrate that the three facial recognition models are not effective at identifying impersonators who use images generated by target identity attacks.

Conclusions

Advancements in digital technology have significantly increased the number of cases involving the counterfeiting of exam identity documents. As a result, automated facial recognition systems are deployed at examination centers to match the registered facial images of candidates against the facial images of prospective examinees.

The novel identity target attack described in this chapter introduces perturbations in the facial image of the real candidate to create a ma-nipulated image that looks just like the real candidate, but tricks an automated facial recognition system by outputting the identity features of the imposter who plans to take the exam on behalf of the candidate. Experiments using 3,000 image pairs from the Labeled Faces in the Wild (LFW) dataset demonstrate the effectiveness of target identity attacks in white-box as well as black-box scenarios.

Future research will focus on developing an algorithm that detects manipulated images and localizes the manipulated regions. Additionally, research will attempt to characterize the properties of manipulated images in the forensic context to identify the specific technique used for manipulation. This will help develop advanced facial authentication systems that are robust to attacks and provide forensically-sound evidence of manipulation.

Figure 1 .

 1 Figure 1. Identifying an imposter.

Figure 2 .

 2 Figure 2. Successful target identity attack.

Figure 3 .

 3 Figure 3. Morphed images generated using four methods.

Figure 4 .

 4 Figure 4. Target identity attack.

  VGGFace and ResNet50 models with VGGFace model perturbations.

Figure 5 .Figure 6 .Figure 7 .Figure 8 .

 5678 Figure 5. Comparison of impostor distance score distributions.

  ROC plots for cosine distance.

Figure 9 .

 9 Figure 9. ROC plots for the ResNet50, VGGFace and LCNN-29 models.

Table 1 .

 1 Summary of experiments.

	Experiment	Dataset Model	Distance Metric
	Target Identity Attacks	LFW VGGFace, ResNet50 Euclidean, Cosine
	Impersonator Recognition with Pre-Trained Models	DFW	VGGFace, ResNet50, LCNN-29	Euclidean, Cosine
	Disguised Faces in the Wild (DFW) Dataset: This dataset
	contains 11,157 facial images of 1,000 subjects. Four types of im-
	ages -normal, validation, disguised and impersonator -are in-
	cluded for each subject.

Table 2 .

 2 Imposter mean distance scores for the white-box and black-box scenarios.

		Euclidean Distance			Cosine Distance
		ResNet50	VGGFace	ResNet50	VGGFace
		Before After Before After Before After Before After
	ResNet50	1.20	0.30	0.91	0.77	0.72	0.08	0.42	0.31
	VGGFace	1.20	1.06	0.91	0.30	0.72	0.62	0.42	0.08


Marcus Nohlberg

Joakim Kävrestad

Exploring Information Security and Domestic Equality

Keywords: Exploring Information Security and Domestic Equality Marcus Nohlberg, Joakim Kävrestad security behaviour, gender, equality, domestic equality

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In terms of security behavior, it is well established that there are differences between men and women. For instance, [START_REF] Lévesque | Age and gender as independent risk factors for malware victimisation[END_REF] [START_REF] Lévesque | Age and gender as independent risk factors for malware victimisation[END_REF] shows that men are more likely than women to encounter malware and [START_REF] Bansal | Moral beliefs and organizational information security policy compliance: The role of gender[END_REF] [START_REF] Bansal | Moral beliefs and organizational information security policy compliance: The role of gender[END_REF] shows that gender plays a role in information security non-compliance intention. [START_REF] Mcgill | Gender differences in information security perceptions and behaviour[END_REF] [START_REF] Mcgill | Gender differences in information security perceptions and behaviour[END_REF] reports that men exhibit an overall stronger security behavior then women, especially for technically oriented tasks such as installing security software. However, they do not find such differences in less technical security tasks such as securing devices with passwords and using security software once it has been installed. [START_REF] Mcgill | Gender differences in information security perceptions and behaviour[END_REF] [START_REF] Mcgill | Gender differences in information security perceptions and behaviour[END_REF] also reports that they do not find any differences in security self-efficacy, a result that conflicts with for instance Anvar et el. (2017) [START_REF] Anwar | Gender difference and employees' cybersecurity behaviors[END_REF] in which differences were found, primarily in security self-efficacy, but also within experience and general computer skills. In a broader perspective, [START_REF] Peacock | Gender inequality in cybersecurity: Exploring the gender gap in opportunities and progression[END_REF] [START_REF] Peacock | Gender inequality in cybersecurity: Exploring the gender gap in opportunities and progression[END_REF] reports that respondents of their survey considered computer security to be a "man' s job" and it is obvious that women are significantly underrepresented in the professional security community.

As described by [START_REF] West | I'd blush if i could: closing gender divides in digital skills through education[END_REF] [START_REF] West | I'd blush if i could: closing gender divides in digital skills through education[END_REF], digital competences are no longer optional but required and the ability to use technology is increasingly important to a person's well-being. It is evident that skills in ICT is a necessity in order to fully participate in modern society and those who participate more enjoy advantages over those who do participate less [START_REF] Bergström | Digital equality and the uptake of digital applications among seniors of different age[END_REF]. [START_REF] Sey | Taking stock: data and evidence on gender equality in digital access, skills, and leadership[END_REF] [START_REF] Sey | Taking stock: data and evidence on gender equality in digital access, skills, and leadership[END_REF] describes that skills in security is one aspect that is needed for participation in the digital world making research into gender-based differences in personal security an important equality issue. We argue that equal, or similar participation and influence over digital matters is an important aspect of equality in a household, as well as in the workplace.

A darker aspect that is affected by security behavior is digital abuse. While physical domestic abuse is a well-known phenomenon to the wider public, the digital era brings other forms of abuse that can to a degree be tied to security behavior. Gagnier (2018) [START_REF] Gagnier | Cyber exploitation and perpetration of digital abuse. Family & Intimate Partner Violence Quarterly[END_REF] described that digital abuse can for instance include identity theft, unlawful surveillance and cyberstalking, and the perpetrator is often a current or previous partner. Al-Alosi (2017) [START_REF] Al-Alosi | Cyber-violence: Digital abuse in the context of domestic violence[END_REF] reports that using different methods to get access to, and control, over the victims' digital accounts on social media, e-mail etc. is a common part of digital abuse. The ways used to get this control range from active hacking practices, such as using spyware, to asking for the passwords to said services. The extent of this phenomenon is hard to quantify, but recent studies indicate that as many as 10 % uses apps or software to spy on their current or former partners' messages, photos, phone calls etc. [START_REF]Nearly half of americans admit to 'stalking' an ex or current partner online[END_REF]. It is also clear that men are overrepresented as the part that monitors women. While it is hard to differentiate normal and trusting sharing of information in a relationship from sharing that can enable digital abuse, we argue that domestic digital inequality can be an enabler for digital abuse.

Gender differences in information security is an under-researched area [START_REF] Mcgill | Gender differences in information security perceptions and behaviour[END_REF] and gender differences in personal security skill and behavior is an equality matter. The aim of this paper is to look into equality factors of personal information security, such as password/account sharing and influence over decisions about digital devices and services in the household as well as perceived ICT skills. The paper intends to shed light on how digitalization works in households in order to problematize around equality in the digital era.

Methodology

The aim of this paper was meet using a web-based survey, built and distributed using Limesurvey, that contained Likert-style questions around the following four themes:

-[influential] How influential the respondents are when it comes to ICT-related decisions in the household. Each question theme consisted of a number of questions, each with a fivegraded answer scale.

In analysing the responses, all answers were dichotomized. The answers "Fully agree" and "Mostly Agree" was converted to 1 and the other options to 0. Thus, 1 represent an agreeing answer. The dichotomized variables were used to created indexes reflecting the respondents score in each theme. For data analysis, the survey data was grouped based on the respondents reported gender. It is reasonable to assume that IT-professionals will answer differently than others [START_REF] Siponen | Five dimensions of information security awareness[END_REF]. Thus, all responses from respondents reporting to be IT-professionals was grouped, and all metrics reported with and without the answers from the group of IT-professionals. The Shapiro-Wilks test was used to test whether the generated data was normally distributed [START_REF] Mendes | Type i error rate and power of three normality tests[END_REF], and the means and median are reported for the indexes. Based on central tendencies observed using descriptive statistics, hypothesis testing was used to evaluate if the tendencies were significant. Because of space limitations, the results are presented in condensed form. The hypotheses were expressed as follows:

H1: There is a difference between the gender groups regarding index X H0: There is no difference between the gender groups regarding index X Further, Mann-Whitney U-test was used for hypothesis testing. Mann-Whitney U-test was selected in favor of T-test since no samples was normally distributed and is therefore more suitable than T-test [START_REF] Mcknight | Mann-whitney u test[END_REF]. The significance level used in this study is the conventional 95% meaning that results are significant if p <0.05. SPSS was used for statistical analysis.

Results

The survey was distributed as an online survey and 152 respondents completed the survey. The answers were used to compute and analyze indexes for the four different themes of interest in this paper. The reminder of this chapter will describe how the indexes was calculated, and the results of the analysis.

Influential Index

Under the influential theme, the respondents were asked the following questions: Each question had a five-graded answer scale labelled as follows; 1 = My partner is much more influential than me, 3=me and my partner are equally influential,5 = I am much more influential than my partner. The answers to the questions were dichotomized so that the two most positive answer options were represented by 1 and the three least positive answers were represented by 0. An index of all questions was computed by adding the variables together. To exemplify, if a respondent provided positive answers to all four questions, the index value became 4, if a respondent provided positive answers to two of the questions, the index value became 2. Table 1, below, presents the mean and median values for male and female respondents. The results are first presented for the entire sample (all) and then only for respondents reporting that they are not IT-professionals (NoIT). As seen in Table 1, male respondents report being more influential than female respondent. The same tendency is seen even if respondents who report being IT-professionals are removed from the analysis. The results of the Mann-Whitney U-test show that the difference is significant showing that the hypothesis "There is a difference between the gender groups regarding index influential" holds.

Sharing Index

Under the sharing theme, the respondents were asked the following questions:

-When you are in a committed relationship, how likely are you to share passwords to social networks (Facebook, Instagram etc) with your partner? -When you are in a committed relationship, how likely are you to share passwords to your personal e-mail with your partner? -When you are in a committed relationship, how likely are you to share passwords to work related accounts with your partner? -When you are in a committed relationship, how likely are you to share passwords/pins to your computer with your partner? -When you are in a committed relationship, how likely are you to share passwords/pins to your cellphone with your partner? -When you are in a committed relationship, how likely are you to share passwords to "streaming services" (Spotify, Netflix, HBO etc) with your partner?

-When you are in a committed relationship, how likely are you to share passwords to online stores (Amazon, Steam, Ebay etc) with your partner?

Each question had a five-graded answer scale labelled as follows; 1=very unlikely, 3=neutral, 5=very likely. An index was computed using the same procedure as previously, resulting in an index that ranged from 0 to 7. Table 2, below, presents the mean and median values for male and female respondents. The results are first presented for the entire sample (all) and then only for respondents reporting that they are not IT-professionals (NoIT). As seen in table 2, a slight tendency suggesting that female respondents are somewhat more prone to share their passwords is shown. However, the results are not significant meaning that the null hypotheses, that there is no observable difference, cannot be discarded.

Autonomy Index

Under the autonomy theme, the respondents were asked the following questions:

-When you receive a new digital device, such as an apple TV, home router or likewise. How likely is it you will install it yourself? -When you receive a new digital device, such as an apple TV, home router or likewise. How likely is it that you will ask your partner for help installing it together with you? -When you receive a new digital device, such as an apple TV, home router or likewise. How likely is it you will ask a friend or family member for help installing it together with you? -When you receive a new digital device, such as an apple TV, home router or likewise. How likely is it that you will ask your partner to install it for you (you will not take part in the installation)? -When you receive a new digital device, such as an apple TV, home router or likewise. How likely is it that you will ask a friend or family member to install it for you (you will not take part in the installation?

Each question had a five-graded answer scale labeled as follows; 1=very unlikely, 3=neutral, 5=very likely. An index was computed using the same procedure as previously, except that the first question was computed as a one for answers 1 or 2. This resulted in an index that ranged from 0 to 5 that reflects how likely the respondents are to get help with the installation of new digital devices. Table 3, below, presents the mean and median values for male and female respondents.

The results are first presented for the entire sample (all) and then only for respondents reporting that they are not IT-professionals (NoIT). As seen in Table 3, female respondents report being more prone to ask for help than male respondents. The same tendency is seen even if respondents who report being IT-professionals are removed from the analysis. The results of the Mann-Whitney U-test show that the difference is significant showing that the hypothesis "There is a difference between the gender groups regarding index autonomy" holds.

Router Skill Index

Under the router skill theme, the respondents were to rate the following statements about their home router using a five graded scale labeled; 1=do not agree at all, 3=neutral, 5=agree fully. This short description of what a home router is was also supplied; The router is the device that that connects your home with the Internet and provides WiFi. An index was computed using the same procedure as previously, resulting in an index that ranged from 0 to 4 Table 4, below, presents the mean and median values for male and female respondents. The results are first presented for the entire sample (all) and then only for respondents reporting that they are not IT-professionals (NoIT).

-I know my WiFi password -I know how to login to the router -I feel comfortable changing the WiFi password -I feel comfortable doing advanced configuration tasks As seen in Table 4, male respondents report being more comfortable with configuring the home router than female respondents. The same tendency is seen even if respondents who report being IT-professionals are removed from the analysis. The results of the Mann-Whitney U-test show that the difference is significant showing that the hypothesis "There is a difference between the gender groups regarding index router skill" holds.

Conclusions

The aim of this paper is to look into equality factors of personal information security, such as password/account sharing and influence over decisions about digital devices and services in the household as well as perceived ICT skills. The study was performed using a survey were respondents were asked about their security behavior and asked to rate their skills in relation to their home router. The answers were used to compute indexes intended to reflect:

-How influential the respondents are regarding domestic ICT decisions -To what degree the respondents share passwords with their partners -How autonomic the respondents are when it comes to installing new ICT devices -How comfortable the respondents are with their home router The results suggest that men are more influential than women when it comes to domestic ICT decisions, as well as more autonomic when it comes to installing new ICT devices and are more comfortable carrying out tasks on the home router. The results of the survey were statistically significant concerning these conclusions. The results could not find any differences in terms of password sharing even if an insignificant tendency, that females shared passwords to a greater level then males, was observed.

As with all surveys that collect self-reported data, socially desirable responding (SDR) bias should be considered a risk [START_REF] Van De Mortel | Faking it: social desirability response bias in self-report research[END_REF]. In this particular case, it is interesting to consider the possible connection between SDR bias and self-efficacy. It is suggested in the literature that computer security is generally considered to be a man's job [START_REF] Peacock | Gender inequality in cybersecurity: Exploring the gender gap in opportunities and progression[END_REF] and it could be interpreted as socially desirable for males to be good at security and for females to be bad at it. This aligns well with the authors' experience of teaching in the technical domain. Although not scientifically researched, male students we talked to report that male students are never questioned about their choice to follow a career in IT while female students very frequently have to justify their decision to family and friends. As such, the possible prevalence of an SDR bias in this context could in itself be a sign of equality problems that need to be addressed in a broad societal context. There might also be a reversed problem among males. If it is perceived to be unmanly to ask for help in technical domains, this may lead to men actually spending more time to learn about these matters, as well as helping others, predominantly women, and thus actually having more knowledge. It might however also lead to under-reporting and perhaps even worse, a general false self-image of competence among males leading to ignorance related to unsafe behavior.

In this study we took, by necessity, a traditional hetero-normative approach to the concept of gender and asked about male and female; this is not a statement in itself, but a choice made due to the fact that we expected to not receive enough answers to have any relevant data to work with anything else than the two traditional genders. We did take care to not assume that people were living in a heteronormative relationship by asking for "partners" and not boyfriend/girlfriend etc. so the study is not focused on the type of relationship a person might be in, but the self-experienced gender dynamics themselves.

We do argue that this, and other research, indicates that gender has a significant impact on security behavior, and it is thus crucial to study this further in the future. Building only on this study, in which we only focused on the subjects' own self-reporting of their own competency, without any actual testing of knowledge. It would be interesting to measure self-image combined with actual knowledge in a larger domain to see if there is an actual difference between the genders, as well as doing interviews trying to get a deeper understanding on if, and why, these differences exit. Even if our results focus on the home environment, those values and differences might also be relevant in the work environment, and relevant to investigate further.

As this study took a binary approach to gender (male/female) it would be very interesting to extend this research into other genders, especially if a large enough dataset can be acquired.

  -[sharing] To what degree the respondents share passwords to various services with their partners. -[autonomy] To that extent the respondents install ICT devices themselves or ask for help -[router skill] To what degree the respondents are comfortable carrying out various tasks on their home router.

-

  When you are in a committed relationship, how influential are you when you decide what streaming service to use (Netflix, HBO etc)? -When you are in a committed relationship, how influential are you when you decide what Internet service provider to use? -When you are in a committed relationship, how influential are you when you decide what new computer/cell phone to buy for yourself? -When you are in a committed relationship, how influential are you when you decide what new common use digital devices to buy together?

Table 1 .

 1 Statistics in the "influential" theme. Results are significant if p <0.05

	Group	Mean Median Mann-Whitney U-test
	All Male (n=99) All Female (n=53) 2.15 3.04	3 2	p=0.000
	NoIT Male (n=54) 2.73 NoIT Female (n=38) 1.84	3 1	p=0.002

Table 2 .

 2 Statistics in the "sharing" theme. Results are significant if p <0.05

	Group	Mean Median Mann-Whitney U-test
	All Male (n=99) All Female (n=53) 3.48 3.30	3 3	p=0.573
	NoIT Male (n=54) 2.72 NoIT Female (n=38) 3.02	2 3	p=0.458

Table 3 .

 3 Statistics in the "autonomy" theme. Results are significant if p <0.05

	Group	Mean Median Mann-Whitney U-test
	All Male (n=99) All Female (n=53) 1.87 1.20	0 2	p=0.017
	NoIT Male (n=54) 0.60 NoIT Female (n=38) 1.66	0 2	p=0.001

Table 4 .

 4 Statistics in the "router skill" theme. Results are significant if p <0.05

	Group	Mean Median Mann-Whitney U-test
	All Male (n=99) All Female (n=53) 2.92 3.48	4 3	p=0.001
	NoIT Male (n=54) 3.30 NoIT Female (n=38) 2.56	4 3	p=0.001