Rita Harb
email: rita.harb@mines-paristech.fr

Rodrigo Rivera-Tinoco
email: rodrigo.rivera_tinoco@mines-paristech.frmaroun.nemer@mines-paristech.fr

Maroun Nemer

Barbar Zeghondy
email: barbarzeghondy@usek.edu.lb

Chakib Bouallou
email: chakib.bouallou@mines-paristech.fr

Towards synthetic fuels production from biomass gasification: tar content at low temperatures

Keywords: tar removal, biomass gasification, thermodynamic properties, psychrometric chart

Biomass conversion into biofuels and biogas is a promising way for power and heat generation. Lately, research has focused on the production of biomethane based on biomass gasification followed by methanation as an alternative to fossil natural gas. The challenge of this process remains in the intensive gas cleaning and required tar removal. In order to study tar removal from the producer gas downstream biomass gasification, solid-vapor and liquid-vapor equilibrium must be known for different tar concentrations, ranging between 0.001 and 100 g.Nm -3 . To assess tar removal by condensation at low temperatures, psychrometric charts are developed. Since the composition of tar is complex, toluene, phenol, indene, naphthalene and fluoranthene are selected as tar representatives. Each component is studied separately in four different producer gas compositions formed mainly of hydrogen, carbon monoxide, carbon dioxide and methane in addition to water and nitrogen. Methods for calculating saturation, isenthalpic and constant relative concentration lines are presented. Psychrometric charts of the different tar components are finally plotted at atmospheric pressure. Results show that temperature of 196.15 K is required to reduce the tar content to 0.001 g.Nm -3 .

Introduction

With the growing interest in fighting against global warming and carbon dioxide emissions, researchers are looking to replace fossil fuels by renewable energy sources. Biomass is one of these energy sources that can serve as a fossil fuel alternative. Converting biomass into bioenergy can be done through biomass gasification. Gasification process converts organic matter into a mixture of gas, tar and char using steam, oxygen, or air as a gasifying agent. The gas is a mixture of carbon monoxide (CO), carbon dioxide (CO2), hydrogen (H2), steam (H2O), nitrogen (N2) and methane (CH4) in addition to tar vapors and some other impurities [START_REF] Patra | Process simulation of hydrogen rich gas production from producer gas using HTS catalysis[END_REF]. This gas mixture is known as producer gas and its composition depends on the used gasifying agent as well as on the gasifier type. The end use of the producer gas is limited due to the formation of multiple by-products, such as NOx, SOx, fly ashes and tars within the gasification process [START_REF] Anis | Tar reduction in biomass producer gas via mechanical, catalytic and thermal methods: A review[END_REF].

Tar is defined, according to the tar measurement protocol [START_REF] Maniatis | Tar Protocols. IEA Bioenergy Gasification Task[END_REF], as hydrocarbons with molecular weight higher than benzene. It is hard to predict the composition of the tar produced during gasification since it depends on many factors such as the type of biomass, gasifying agent, reactor type, operating temperature and pressure. Thus, to facilitate the comprehension of tar composition, tar can be classified according to different characteristics. The most adapted classification is presented in Table 1 where tar compounds are divided into 5 classes according to their condensability and solubility.

After biomass gasification, the tar content in the producer gas varies between 1 g.Nm -3 and 100 g.Nm -3 according to the adapted process [START_REF] Bermudez | Production of bio-syngas and bio-hydrogen via gasification[END_REF] even if primary methods for tar removal were integrated within the gasifier. Thus, in all cases, secondary methods are required to reduce the tar content to 1 mg.Nm -3 . Up to now, very few researchers were able to reduce the tar content to this level. The elimination of tar by a single step via a physical/mechanical method is not efficient [START_REF] Valderrama Rios | Reduction of tar generated during biomass gasification: A review[END_REF][START_REF] Phuphuakrat | Absorptive removal of biomass tar using water and oily materials[END_REF] and results in a tar content between 5 g.Nm -3 and 30 g.Nm -3 . Combining two physical methods for tar treatment increases its removal efficiency and yields a producer gas with a tar content around 250 mg.Nm -3 , noting that benzene and toluene were not taken into account [START_REF] Zwart | Oilbased gas washing-Flexible tar removal for high-efficient production of clean heat and power as well as sustainable fuels and chemicals[END_REF]. Tar reduction via thermal cracking reduces the tar content to a value between 5 and 7 g.Nm -3 at high temperatures (1523 K) leading to a high operating cost [START_REF] Brandt | Decomposition of tar in gas from updraft gasifier by thermal cracking[END_REF]. Catalytic cracking seems to be a promising method for tar reduction. This method eliminates around 99% of tars, yielding a tar content of 0.16 g.Nm -3 . However, the high tar removal rate is limited in time due to the rapid deactivation of the catalyst within few hours of operation as a result of coke deposition [START_REF] Lu | Effect of operating conditions on the coke formation and nickel catalyst performance during cracking of tar[END_REF][START_REF] Pallozzi | Combined gas conditioning and cleaning for reduction of tars in biomass gasification[END_REF]. The highest tar removal efficiencies were achieved by combining at least two operational units. The first unit is assimilated to a pre-treatment step. It increases the lifetime of the second step and reduces its cost by removing the heavy fraction of tar, while lighter tar compounds are being removed in the second step. Some examples of these two-step techniques are OLGA, which combines a collector followed by an absorber [START_REF] Boerrigter | OLGA" tar removal technology: Proof of-concept for application in integrated biomass gasification combined heat and power (CHP) systems[END_REF], oil or water scrubber followed by activated carbon bed [START_REF] Thunman | Advanced biofuel production via gasification -lessons learned from 200 man-years of research activity with Chalmers' research gasifier and the GoBiGas demonstration plant[END_REF], thermal cracking followed by an adsorption bed [START_REF] Phuphuakrat | Tar removal from biomass pyrolysis gas in twostep function of decomposition and adsorption[END_REF] and absorption followed by catalytic cracking [START_REF] Pallozzi | Combined gas conditioning and cleaning for reduction of tars in biomass gasification[END_REF]. It was also seen that the reduction of tar content in most of the cases was insufficient for combining gasification and methanation.

Table 1

Tar classification with respect to their molecular weight [START_REF] Valderrama Rios | Reduction of tar generated during biomass gasification: A review[END_REF][START_REF] Guan | Catalytic steam reforming of biomass tar: Prospects and challenges[END_REF][START_REF] Weston | Tar Destruction in a Coandă Tar Cracker[END_REF].

Tar

Class

Class Name Property Compounds The potential use and temperature ranges of the two steps low temperature removal process, for reducing the tar content down to 1 mg.Nm -3 , is assessed based on the condensing temperatures of tars. The first step operates at room temperature, while the second one operates at lower temperatures. Adapting low temperature ensures the condensation of tars by cooling the producer gas to a temperature below tar dew point for a concentration of 1 mg.Nm -3 . Different ways could be adapted in order to cool the producer gas. Suggesting the use of low temperature tar removal offers the possibility to reduce dramatically the content of light tars in the producer gas, while generating condensate fractions of potential market value. Handling condensates, however, remains the main drawback of low temperature tar removal. Formation of solid phase layers could lead to clogging of heat exchangers, as well as a dramatic reduction on heat transfer. For this last, the energy efficiency of the process would decline rapidly and render the process economically unviable. Managing the solid phase formation in heat exchangers is a topic for which technological solutions may be available, for example, de-waxing pig systems (pipe cleaning). Another option is to cycle the temperature of heat exchangers. However, the analysis of such solutions are beyond the scope of this paper.

In order to analyze the condensation behavior of tar components at low temperature and assess the energy required to condense tar compounds, psychrometric charts for tar components in producer gas are plotted. Aspen Properties V10 was used to find the phase equilibrium composition at saturation, as well as assessing the impact of gas mixtures compositions at different temperatures. Equations were developed for calculating the enthalpy of mixtures and the constant relative concentration lines. As mentioned earlier, the producer gas is a multicomponent mixture of CO, CO2, H2, N2, CH4, H2O and some impurities. This composition is affected by the gasification process and its parameters. The psychrometric charts are used to illustrate the tar content in the producer gas at different temperatures and the energy required for condensing tars at different concentrations.

As a part of the state of the art, an open source tool developed by the Energy research Center of the Netherlands (ECN) is available online [START_REF]Thersites: Complete model[END_REF]. This tool enables the calculations of the dew point for a single tar component or a mixture of tar components. It was developed to calculate the temperature at which tar condensation starts for a given tar content expressed in g.Nm -3 on wet basis [START_REF] Rabou | Tar in biomass producer gas, the energy research centre of the netherlands (ECN) experience: an enduring challenge[END_REF]. Tar vapors are assumed as ideal gas and have an ideal mixing behavior. The main disadvantage of this tool is that the composition of the producer gas is not well defined and cannot be modified; hence, dew point calculations are limited.

In order to study the impact of the producer gas composition on the condensation behavior of tar components, the dew point of tar components is studied for four different producer gas compositions. Those compositions are selected in a way to cover producer gas compositions obtained from several types of gasifiers and gasifying agents. This enables the comparison of the simulated results with those obtained from the ECN tool. Then, a special attention will be given to the energy required for the condensation of tar by finding the enthalpy of mixtures at several points. The selected physical property method to be used in Aspen simulations and calculations is Non-Random Two-Liquid model (NRTL) since it can handle any mixture of polar and nonpolar components. It is widely used for phase equilibria calculations (vapor-liquid and liquidliquid equilibrium) [START_REF] Aspentech | Aspen Physical Property Methods[END_REF]. This paper is organized as follows: methodology and assumptions are presented in section 2, results are shown in section 3; finally, conclusions are drawn and perspectives are provided in section 4.

Assumptions and methodology

In this paper, four producer gas compositions are considered in order to study their impact on tar dew point. Single tar component will be considered at each time. The selection criteria of tar components and producer gas composition are explained later on.

Tar representative components

Since tar is a complex mixture, some tar compounds are selected as representatives components per class for which psychrometric charts are plotted separately. The criteria for the selection of tar representative components are:

• the lightest component in each class should be selected, thus the rest of the components in the same class will condense eventually at higher temperatures. • the fraction of the component should not be negligible. Table 2 summarizes some physical properties, the class and the mass percentage of the most common components forming the tar. Based on the selection criteria, it can be deduced from Table 2 that tar representative components are phenol for class 2, toluene for class 3, indene and naphthalene for class 4 and fluoranthene for class 5. Two components from class 4 were considered since indene is the lightest one, while naphthalene is present in an important amount and it was considered as tar representative in other studies [START_REF] Phuphuakrat | Absorptive removal of biomass tar using water and oily materials[END_REF][START_REF] Alamia | Performance of large-scale biomass gasifiers in a biorefinery, a state-of-the-art reference[END_REF][START_REF] El-Rub | Experimental comparison of biomass chars with other catalysts for tar reduction[END_REF].

Producer gas composition

As previously mentioned, different producer gas compositions are studied in order to check their impact on the dew point of tar components. The main variation in the composition is the mole fraction of steam (H2O) and nitrogen (N2). Steam gasification, for direct and indirect gasifier, yields a producer gas with a high moisture content (30 to 60 mol% on wet basis). While air gasification yields a producer gas with a high nitrogen content (40 to 60 mol% on dry basis). The molar fraction of the remaining components is also affected by the gasifying agent. However, this variation is minor in comparison with the variation of the steam and nitrogen content. Table 3 illustrates the different producer gas compositions used in the calculations to build the psychrometric charts. The first case in Table 3 corresponds to a dual fluidized bed (DFB) gasifier or indirect gasifier where wood-based biomass is being gasified using steam is the gasifying agent [START_REF] Thunman | Advanced biofuel production via gasification -lessons learned from 200 man-years of research activity with Chalmers' research gasifier and the GoBiGas demonstration plant[END_REF]. This gasifier yields a producer gas composition suitable for methanation purposes, as it is inert free with high hydrogen to carbon ratios [START_REF] Alamia | Performance of large-scale biomass gasifiers in a biorefinery, a state-of-the-art reference[END_REF][START_REF] Van Der Meijden | Wood to Bio-Methane demonstration project in the Netherlands[END_REF]. The second and the third cases correspond to a bubbling fluidized bed gasifier operating with air as a gasifying agent. Case 2 [START_REF] Gil | Biomass gasification in atmospheric and bubbling fluidized bed: Effect of the type of gasifying agent on the product distribution[END_REF] corresponds to the gasification of pine wood at 1073 K with an equivalence ratio (ER) of 0.25. For case 3 [START_REF] Van Paasen | Gasification of Non-woody Biomass[END_REF], refused-derived fuel (RDF) is gasified at 1093 K with an ER equal to 0.21. The last case (case 4) represents a sawmill waste downdraft gasifier that uses air as a gasifying agent yielding a producer gas with a low water and high inert contents [START_REF] Hassan | A preliminary investigation of compressed producer gas from downdraft biomass gasifier[END_REF]. The calculated dew points of tar compounds are compared with those obtained from the online tool provided by ECN for the same tar concentration. The composition of the producer gas used by ECN for the calculations of the tar dew point based on tar concentration is not given, but the calculations were based on an atmospheric bubbling fluidized bed gasifier operating at 1123 K. The calculation of tar dew point, forming the saturation line, is explained in the sequel.

The presence of large amounts of water in the producer gas affects tar dew point as well as the enthalpy, mainly for temperatures lower than the water dew point. The initial tar content was set to 100 g.Nm -3 . Note that the presence of water in the mixture imposes the use of a suitable property method for polar mixtures. In this paper, NRTL is used.

Table 3

Different raw gas compositions in wet basis used for the calculation of tar dew point.

Case 1 [17] Case 2 [29] Case 3 [30] Case 4 [31]

Saturation and constant relative concentration lines

Calculations are based on an initial concentration of 100 g of tar.Nm -3 of raw gas for each tar component. This concentration was selected since it is equal to the highest tar level that could be obtained from the gasification. In this section, "raw gas" term corresponds to the mixture of components forming the producer gas excluding tar compounds. Same calculations are repeated for each one of the previously selected tar components: phenol, toluene, indene, naphthalene and fluoranthene. Points of the saturation line for each case were calculated at first. The saturation line, liquidvapor above the triple point and solid-vapor below it, represents the limit at which tar condensation in the producer gas starts. The vapor fraction (VF) was calculated at different temperatures (Ti) in order to detect the temperature at which condensation begins (VF ≠ 1). At this temperature, the gas phase molar fraction (yj) was calculated to compute the amount of tar condensed using a defined property method in Aspen Properties, where j represents the different components of the producer gas. The amount of tar remaining in the gas phase is calculated according to Eq (1) and Eq [START_REF] Anis | Tar reduction in biomass producer gas via mechanical, catalytic and thermal methods: A review[END_REF] where wtar is defined as the tar content, in kg.kg -1 raw gas, and it is equal to the ratio of tar mass (mtar) to the raw gas mass (mraw gas). Tar concentration (Ctar) is deduced from Eq (3) in mg of tar.Nm -3 of raw gas. Same calculations are repeated for different temperatures. The latter decreases with an interval of 10 K until reaching a tar concentration of 1 mg of tar.Nm -3 of raw gas. The equations presented below (from Eq (1) to Eq [START_REF] Woolcock | A review of cleaning technologies for biomass-derived syngas[END_REF]) are analogous to the equations used for the psychrometric chart of humid air, however applied for other components.

=

.

(1)

= = - . (2)
= . 10 (3)
where Mtar and Mraw gas are respectively tar and raw gas molecular masses in g.mol -1 , Psat is the saturated tar partial pressure in the raw gas in Pa, Ptotal is the total mixture pressure and it is equal to the atmospheric pressure in Pa and ρraw gas is the density of the raw gas in kg.m -3 .

Constant tar relative concentration (ϕ) curves are then plotted. ϕ is expressed in Eq (4) and it is equal to the ratio of the tar partial pressure in the raw gas (Ptar) to the saturated tar partial pressure at the same temperature (Ti). To plot those curves, tar partial pressure was calculated at each Ti for a fixed value of ϕ according to Eq [START_REF] Zhang | Tar destruction and coke formation during rapid pyrolysis and gasification of biomass in a drop-tube furnace[END_REF]. Tar vapors are assumed to behave as ideal gases. For each value of ϕ, ranging between 10% and 90%, and for a total pressure of one atmosphere, the tar content was calculated for different temperatures according to Eq [START_REF] Guan | Catalytic steam reforming of biomass tar: Prospects and challenges[END_REF]. Finally, tar content is plotted against the temperature for each value of ϕ.

ϕ = (4) = ϕ. = ϕ. . ( 5
) = ϕ. 1 -ϕ. . (6)

Saturation enthalpy and isenthalpic lines

Isenthalpic lines help in finding, at any condition, the enthalpy of the mixture. They join several points together having an equal enthalpy. The enthalpy of the gas mixture is calculated per kilogram of dry raw gas. Calculations begin by finding the enthalpy at saturation. Plotting enthalpies at saturation versus the temperature yields the saturation enthalpy line. In Fig. 1, points 1 and 2 of the saturation line are considered. Tar contents, dew points and the enthalpies of those points are known. Note that point 1 has the highest dew point. A constant tar content line, drawn as a horizontal line starting from point 2, intersects with the constant enthalpy line of point 1. The intersection point is denoted as point 3. Consequently, the following equations can be concluded: h1=h3, wtar,2=wtar,3 and wwat,2=wwat,3 where wwat corresponds to the water content, in kg.kg -1 raw gas. The enthalpy of the raw gas at any point, expressed in kJ.kg -1 , is given in Eq [START_REF] Woolcock | A review of cleaning technologies for biomass-derived syngas[END_REF]. This equation describes the conventional enthalpy calculation at dry bulb conditions considering mainly gas and liquid phases. As operating temperatures considered for tar removal are below 273 K, the risk of ice and tar deposit formation exists. Even though preliminary calculations show limited discrepancies between calculated and existing data on psychrometric charts for water, the assessment of the impact of considering L-V or S-L-V phases on enthalpy calculations including water and tars is needed. This is beyond the scope of this work and will be dealt with in future as enthalpy miscalculations could lead to overconsumption of cooling utilities, in particular at low temperatures. ℎ = , . + "ℎ # , $%& ' + , ,( . ), * + "ℎ # , $%& ' + , ,( . ), *

where Cp,raw gas is the heat capacity of the mixture excluding water and tar component, Cp,tar,vap and Cp,wat,vap are respectively the heat capacity of the tar component and water in the vapor phase expressed in kJ.kg -1 .K -1 , TDB is the dry bulb temperature expressed in K, hfg,tar is the latent heat of vaporization or the latent heat of sublimation of tar component depending on its triple point expressed in kJ.kg -1 , hfg,wat is the latent heat of vaporization of water expressed in kJ.kg -1 and TDP is the dew point temperature expressed in K. By solving Eq (7) for points 1, 2 and 3, Eq (8) can be deduced. The latter is solved for TDB,3, thus the coordinates of a new point on the isenthalpic line are found. To reach better accuracy, threeto-four points are considered in the plot of the constant enthalpy line. When the condensation takes place at a temperature lower than the triple point (in solid phase), the built-in correlations in Aspen Properties do not consider the solid phase condensation and may yield non-accurate results. Studying the solid condensation behavior for each tar compound separately is very time consuming since the composition of tar is complicated and encloses too many compounds. In addition, developing a solid phase characteristic curve for all the compounds is not necessary. Therefore, we investigate in a general way to develop a solid condensation characteristic curve by expanding the liquid condensation laws into the solid phase.

ℎ & -ℎ $ = , ,& . ,& -, ,$ . ,$ (8)
To justify the decision of expanding the laws into the solid phase, a comparison between the vapor pressure obtained using Aspen and that found in the literature for multiple compounds is done for a defined temperature interval below the triple point. The comparison was done for fluoranthene, naphthalene and phenol. Note that a comparison with the solid vapor pressure of indene was not done since the latter was not found in the literature review. Similarly for the toluene, which triple point is too low. It is important to mention that the comparison was done based on dry basis raw gas composition. Otherwise, the curves could not be compared since the condensation of water affects the tar condensation.

Results and discussion

3.1. Solid -gas equilibrium A comparison between experimental vapor pressures and calculated vapor pressures based on Aspen using built-in correlations for solid -gas equilibrium is illustrated in Fig. 2. The experimental values of vapor pressures for temperatures lower than the triple point for fluoranthene [START_REF] Monte | Thermodynamic properties of fluoranthene: An experimental and computational study[END_REF], naphthalene [START_REF] De Kruif | The vapour pressure of solid and liquid naphthalene[END_REF] and phenol [START_REF] Biddiscombe | Vapour pressures of phenol and the cresols[END_REF] were taken from literature studies. The agreement between the experimental values and the calculated ones shown in Fig. 2 indicates the possibility of extending the correlations used for gas and liquid phases to cover the solid phase. It should be mentioned that the vapor pressure taken from the literature review are for mixtures of fluoranthene, naphthalene and phenol in air and not in producer gas. This might lead to a certain deviation between calculated and experimental vapor pressure values.

Tar dew point

After validating that the correlations of Aspen could be extended to cover also the solid -gas equilibrium, the dew point of tar representative components can be calculated. A comparison between the calculated dew point of those components and the ones retrieved from the ECN tool is completed. The different cases summarized previously in Table 3 are considered to check the impact of the producer gas composition on tar condensation. The calculations are carried out for a single tar component at a time.

T/K

The dew point of fluoranthene (Fig. 3 A) is expected to be the highest one since this latter is the heaviest component. ECN stated that their online tool overestimates the dew point of tar by 7 to 14 K at temperature higher than 440 K. This explains the large deviation between the dew points calculated and those obtained from their online tool at high temperatures. The major part of fluoranthene is removed by condensation before reaching water dew point. The latter depends on the initial moisture content (described in Table 3) varying from 303 to 333 K. It can be concluded that the dew point of tar components is mainly affected by the moisture content and the condensation of steam in the producer gas rather than the composition of the noncondensable gases.

The dew points of naphthalene (Fig. 3 B), indene (Fig. 3 C) and phenol (Fig. 3 D) decrease with the initial moisture content. While for toluene (Fig. 3 E), the higher the initial moisture content, the lower the toluene dew point. Contrary to other tar representative components, toluene with water forms an azeotropic mixture leading to a decrease in the boiling point. This explain the difference in the behavior between toluene and the rest of tar components regarding the variation of the initial moisture content present in the producer gas. The best agreement between the calculated dew point and the ones available online is encountered for phenol and naphthalene. Indene shows a high deviation for high moisture content. The deviation decreases for a low moisture content in the producer gas (6 mol %wet). The same analogy applies for the toluene. The highest deviation is faced for Case 1 which corresponds to the highest moisture content (30.4 mol %wet). The lowest deviation is achieved for Case 4 having the lowest initial moisture content.

Although the deviation decreases with the initial moisture content for toluene, the difference is still high. This may be caused by ice formation at temperatures lower than 273 K. As seen, tar dew point depends on the composition of the producer gas. However, this composition is not defined for the online tool calculator. It is only mentioned that their tool is based on an atmospheric bubbling fluidized bed operating at 1123 K using air as a gasification agent. All these factors increase the deviation between the calculated tar dew points and those obtained through the online dew point calculator.

Psychrometric charts

By completing the calculations for all the mixtures of tar representative components in the raw gas, psychrometric charts for tar-raw gas mixtures were plotted. Calculations in this section are based on Case 1 in Table 3 referring to a DFB gasifier. The latter is selected since it yields a suitable producer gas composition for methanation. The five psychrometric charts of fluoranthene, naphthalene, indene, phenol and toluene -raw gas mixtures are illustrated in Fig. 4, Fig. 5, Fig. 6, Fig. 7 and Fig. 8, respectively. The aim is to obtain a raw gas with a tar content lower than 1 mg.Nm -3 . It can be deduced that a temperature around 193 K is required to reach it. Note that the enthalpy difference calculated between two points is more significant than the one calculated at each point since it reflects the required energy for condensing a certain mass of tars. To find the enthalpy of a point on the charts that is not at saturation, the isenthalpic lines are projected until they reach the enthalpy line at saturation (bold black dashed line). The intersection between the projection and the enthalpy at saturation line is then read on the right axis. This value corresponds to the enthalpy in kJ.kg -1 . In Fig. 4, Fig. 5, Fig. 6, Fig. 7 and Fig. 8, the light grey lines represent the projection of the isenthalpic lines for better understanding. At a tar content of 100 g.Nm -3 , the condensation of water and the tar representative component take place simultaneously, except for the heaviest one (fluoranthene) and the lightest one (toluene). The condensation of fluoranthene, toluene and water starts at a temperature of 476 K, 253 K and 355 K, respectively. In other words, fluoranthene condenses prior to water while toluene condenses after it. It can be deduced, from the figures below, that the condensation of water has a higher impact on the enthalpy change. This is justified by the increase in the slope of the enthalpy for temperatures lower than that of water dew point. According to Fig. 4, the enthalpy varies linearly function of temperatures greater than 355 K. This section presents the condensation of fluoranthene only. Below this temperature, water condensation starts, leading to a higher enthalpy variation thus a steeper enthalpy slope. Moreover, the water fraction is higher than the tar fraction in the producer gas, thus the impact of water condensation on the enthalpy is higher. Moving to indene, naphthalene and phenol, the slope of the enthalpy is notably higher than that of fluoranthene. According to Fig. 4 and Fig. 6, to reduce the tar content (w) from 0.06 to 0.02 kg.kg -1 , the energy required for fluoranthene is equal to 98.7 kJ.kg -1 while that of indene is much higher (217.4 kJ.kg -1 ). Finally, the condensation of toluene begins after the removal of a high fraction of water. As a result, the energy required for toluene condensation to reduce the content from 0.06 to 0.02 kg.kg -1 is the lowest one (28.519 kJ.kg -1 ), as seen in Fig. 8. We can observe in Fig. 4, Fig. 5, Fig. 6, Fig. 7 and Fig. 8 that, at low temperatures, the charts 345 have very small values and therefore, the tar content is not readable.

Conclusion

In this paper, four different producer gas compositions were studied to check their impact on tar removal. The study involves the calculation of the dew point of several components besides water, that are fluoranthene, naphthalene, indene, phenol and toluene. Each component was separately studied with the producer gas. The dew point was calculated for tar concentrations ranging between 0.001 and 100 g.Nm -3 . Psychrometric charts were plotted for the gas composition of dual fluidized bed gasifier to represent gas stream thermodynamic properties variations during tar capture by applying low temperatures. As a result, the dew points of fluoranthene, naphthalene, indene, phenol and toluene vary respectively from 325 to 473 K, 245 to 374 K, 237 to 359 K, 240 to 361 K and 196 to 262 K. In addition, the high level of required energy for tar removal at temperature lower than the water dew point is a result of water existence in the producer gas. As for future work, we aim at using the obtained results to study the condensation of tar on a vertical plate surface, which could be beneficial in evaluating the tar and water removal efficiency.

Fig. 1 .

 1 Fig. 1. Illustration of the constant enthalpy line calculations.

Fig. 2 .

 2 Fig. 2. Comparison between the experimental vapor pressures (VP) and the calculated ones based on Aspen correlations for A) fluoranthene [32], B) naphthalene [33] and C) phenol [34] at temperatures lower than the triple point.

Fig. 3 Fig. 3 .

 33 Fig. 3. Dew point of A) fluoranthene, B) naphthalene, C) indene, D) phenol and E) toluene in producer gas mixtures for different compositions.

Fig. 4 .Fig. 5 .Fig. 6 .

 456 Fig. 4. Psychrometric charts of fluoranthene-raw gas mixture.

Fig. 7 .Fig. 8 .

 78 Fig. 7. Psychrometric charts of phenol-raw gas mixture.

Table 2

 2 Physical properties of common tar components.

	Component	Boiling point (K)	Freezing point (K)	Molecular weight (g.mol -1 )	Class	Mass percentage (%) excl. benzene [25-27]
	Benzene	353.24	278.68	78.11
	o-cresol	464.15	304.19	108.10	2	1-10
	2,6-xylenol	474.22	318.76	122.10	3	0.5-2.5
	Toluene	383.78	178.18	92.13	3	8-24
	o-xylene	411.51	286.41	106.10	3	1-10
	Indene	455.77	271.70	116.16	4	2-12
	Phenol	454.99	314.06	94.11	2	6-12
	Naphthalene	491.14	353.43	128.17	4	10-30
	2-mehtylnaphthalene 514.26	307.73	142.19	4	4
	Biphenyl	528.15	362.65	154.20	4	0-2
	Acenaphthalene	543.15	342.35	152.19	4	3-7
	Acenaphthene	550.54	366.56	154.20	4	0.5
	Fluorene	570.44	387.94	166.21	4	0-2
	Phenanthrene	610.03	372.38	178.22	4	2-4
	Anthracene	615.18	488.93	178.22	4	0.2-4
	Fluoranthene	655.95	383.33	202.25	5	1-2.5
	Pyrene	667.95	423.81	202.25	5	0-2

kJ . kg -1 raw gas w indene / kg indene . kg -1 raw gas

	0.12
								ϕ=100%
	raw gas	1					ϕ=90% ϕ=80%
	naphthalene / kg naphthalene . kg -1 w						h naphthalene / kJ . kg -1 raw gas	ϕ=70% ϕ=60% ϕ=50% ϕ=40% ϕ=30% ϕ=20% ϕ=10% h constant at 353 K h constant at 343 K
								h constant at 323 K
								Enthalpy
			273 283 293 303 313 323 333 343 353 363 373
							T/K
	0.09					1200
	0.08 0.07					1000	ϕ=100% ϕ=90%
								ϕ=80%
	0.06					800	ϕ=70%
								ϕ=60%
	0.05						ϕ=50%
							600	ϕ=40%
	0 0.01 0.02 0.03 0.04	273	283	293	303	0 200 400 indene / T/K 313 323 333 343 353 h	ϕ=30% ϕ=20% ϕ=10% h constant at K h constant at K Enthalpy

Table 4 ,

 4 Table 5, Table 6, 346 Table 7 and Table 8 present the tar content and the enthalpy of fluoranthene, naphthalene, 347

	1400
	1200
	1000
	800
	600
	400
	200
	0

toluene /kJ. kg -1 raw gas w toluene / kg toluene . kg -1 raw gas

  Enthalpyindene, phenol and toluene in function of temperature. The more accurate values may be of a great benefit for those working with gasification and tar removal by physical methods. Moreover, they reflect the required temperature for reaching the limit for any application, not only for biomethane production. The values presented in the tables below correspond to the saturation line where ϕ=100%.

	ϕ=100%
	ϕ=90%
	ϕ=80%
	ϕ=70%
	ϕ=60%
	ϕ=50%
	ϕ=40%
	ϕ=30%
	ϕ=20%
	ϕ=10%
	h constant at K
	h constant at K
	T/K

Table 4

 4 Computational mass content (w), concentration (C) and enthalpy (h) at temperature (T) and atmospheric pressure for the mixture of fluoranthene in raw gas.

	T / K	Cfluoranthene	wfluoranthene /	hfluoranthene	T / K	Cfluoranthene	wfluoranthene /	hfluoranthene
		/ mg.Nm -3	x10 -5 kg.kg -1	/ kJ. kg -1		/ mg.Nm -3	x10 -5 kg.kg -1	/ kJ. kg -1
			raw gas	raw gas			raw gas	raw gas
	393.15	1950.30	244.24	1277.07	359.15	136.62	17.27	1156.64
	391.15	1719.08	215.32	1271.17	357.15	109.78	13.90	1144.97
	389.15	1511.45	189.34	1265.25	355.15	86.88	11.03	1131.51
	387.15	1325.35	166.06	1259.28	351.15	51.31	6.56	1095.98
	385.15	1158.86	145.23	1253.25	349.15	38.00	4.88	1071.31
	383.15	1010.19	126.63	1247.16	345.15	19.20	2.51	998.15
	381.15	877.86	110.08	1240.98	343.15	13.26	1.76	946.38
	379.15	760.38	95.37	1234.70	339.15	6.42	0.88	819.66
	377.15	656.28	82.35	1228.29	337.15	4.61	0.65	752.77
	375.15	564.24	70.82	1221.73	335.15	3.39	0.48	687.98
	373.15	483.03	60.66	1214.98	333.15	2.54	0.37	626.96
	369.15	348.78	43.85	1200.73	331.15	1.94	0.29	570.43
	367.15	293.82	36.96	1193.10	329.15	1.50	0.23	518.54
	365.15	245.83	30.95	1185.03	327.15	1.17	0.18	471.15
	361.15	167.87	21.18	1167.00	325.15	0.93	0.14	427.97

Table 5

 5 Computational

mass content (w), concentration (C) and enthalpy (h) at temperature (T) and atmospheric pressure for the mixture of naphthalene in raw gas.

T / K

Cnaphthalene / mg.Nm -3 wnaphthalene / x10 -5 kg.kg -1 raw gas hnaphthalene / kJ. kg -1 raw gas T / K Cnaphthalene / mg.Nm -3 wnaphthalene / x10 -5 kg.kg -1 raw gas hnaphthalene / kJ. kg -1 raw gas

	330.15	1196.61	179.20	527.70	285.15	33.80	5.86	49.18
	327.15	902.32	138.01	458.47	282.15	26.65	4.63	38.82
	324.15	691.93	107.84	397.86	279.15	20.93	3.64	29.41
	321.15	537.34	85.14	345.00	276.15	16.37	2.86	20.83
	318.15	421.21	67.71	298.96	273.15	12.75	2.23	12.94
	315.15	332.45	54.12	258.83	270.15	9.89	1.73	5.65
	312.15	263.68	43.40	223.82	267.15	7.62	1.34	-1.15
	309.15	209.83	34.87	193.22	264.15	5.85	1.03	-7.53
	306.15	167.32	28.04	166.44	261.15	4.46	0.78	-13.56
	303.15	133.55	22.54	142.93	258.15	3.38	0.60	-19.30
	300.15	106.61	18.10	122.26	255.15	2.55	0.45	-24.80
	297.15	85.04	14.52	104.01	252.15	1.91	0.34	-30.10
	294.15	67.75	11.62	87.85	249.15	1.42	0.25	-35.24
	291.15	53.87	9.28	73.50	246.15	1.04	0.18	-40.24
	288.15	42.74	7.38	60.68	245.15	0.95	0.17	-41.88

Table 6

 6 Computational mass content (w), concentration (C) and enthalpy (h) at temperature (T) and atmospheric pressure for the mixture of indene in raw gas.

	T / K	Cindene	/	windene	/	hindene	/	T / K	Cindene	/	windene	/	hindene	/
		mg.Nm -3		x10 -5 kg.kg -1	kJ. kg -1		mg.Nm -3		x10 -5 kg.kg -1	kJ. kg -1
				raw gas		raw gas					raw gas		raw gas
	310.15	839.82		139.19		185.89		272.15	37.86		6.62		10.81
	308.15	721.50		120.28		167.86		270.15	31.42		5.50		6.86
	306.15	619.87		103.89		151.42		268.15	26.02		4.56		3.13
	302.15	457.03		77.31		122.71		266.15	21.48		3.77		-0.40
	300.15	392.01		66.58		110.18		264.15	17.69		3.11		-3.77
	298.15	335.90		57.25		98.71		262.15	14.53		2.55		-6.98
	294.15	245.66		42.13		78.57		260.15	11.90		2.09		-10.05
	292.15	209.59		36.05		69.73		256.15	7.91		1.39		-15.85
	290.15	178.50		30.77		61.59		254.15	6.42		1.13		-18.60
	288.15	151.73		26.22		54.10		252.15	5.19		0.92		-21.27
	286.15	128.70		22.28		47.19		250.15	4.18		0.74		-23.87
	284.15	108.91		18.89		40.80		246.15	2.69		0.48		-28.88
	280.15	77.43		13.47		29.38		244.15	2.15		0.38		-31.31
	278.15	65.03		11.33		24.26		240.15	1.35		0.24		-36.04
	274.15	45.48		7.94		15.01		237.15	0.94		0.17		-39.50

Table 7

 7 Computational mass content (w), concentration (C) and enthalpy (h) at temperature (T) and atmospheric pressure for the mixture of phenol in raw gas.

	T / K	Cphenol	/
		mg.

Nm -3 wphenol / x10 -5 kg.kg -1 raw gas hphenol / kJ. kg -1 raw gas T / K Cphenol / mg.Nm -3 wphenol / x10 -5 kg.kg -1 raw gas

								hphenol	/
								kJ. kg -1
								raw gas
	303.15	1159.37	195.91	118.15	271.15	52.52	9.26	8.52
	301.15	975.65	165.62	106.17	269.15	42.05	7.42	3.74
	299.15	819.18	139.64	95.08	267.15	33.54	5.93	-0.83
	297.15	686.16	117.42	84.81	265.15	26.63	4.72	-5.21
	295.15	573.32	98.45	75.29	263.15	21.06	3.73	-9.43
	293.15	477.79	82.31	66.45	261.15	16.58	2.94	-13.49
	291.15	397.11	68.61	58.22	259.15	12.99	2.31	-17.41
	289.15	329.12	57.01	50.55	257.15	10.13	1.80	-21.23
	287.15	271.97	47.23	43.38	255.15	7.86	1.40	-24.93
	285.15	224.07	39.00	36.67	253.15	6.07	1.08	-28.55
	283.15	184.02	32.10	30.37	251.15	4.66	0.83	-32.09
	281.15	150.64	26.33	24.44	249.15	3.56	0.64	-35.56
	279.15	122.90	21.52	18.85	247.15	2.70	0.49	-38.96
	277.15	99.91	17.53	13.55	245.15	2.04	0.37	-42.32
	275.15	80.94	14.22	118.15	243.15	1.53	0.28	-45.62
	273.15	65.32	11.50	106.17	240.15	0.99	0.18	-50.51

Table 8

Computational mass content (w), concentration (C) and enthalpy (h) at temperature (T) and atmospheric pressure for the mixture of toluene in raw gas.

T / K

Ctoluene / mg.Nm -3 wtoluene / x10 -5 kg.kg - The enthalpy variation is caused by three different processes. The latent heat of the tar component condensation, the latent heat of the water condensation and the sensible heat of the gas. For fluoranthene (Table 4), the variation of the enthalpy at high temperature for 433 -≤ ≤ 473 -, is driven by the latent heat of fluoranthene condensation, where 83% of the initial fluoranthene content is condensed. For temperatures ranging between 355 K and 433 K, the sensible heat of the gas occupies the major fraction of the enthalpy variation. Below 355 K, the enthalpy variation in driven by the latent heat of water condensation. For naphthalene (Table 5), indene (Table 6) and phenol (Table 7), the latent heat of the tar component surpasses the rest for temperatures higher than 355 K. Between 355 K and 283 K, the latent heat of water condensation forms the major part of the enthalpy variation. Below 283 K, the sensible heat surpasses both latent heats. For toluene (Table 8), since its condensation starts below 273 K, the latent heat of toluene condensation drives the enthalpy variation for temperatures higher than 243 K. Below this temperature, the weight of the sensible heat on the enthalpy variation becomes greater than the latent heat.

It can be seen that fluoranthene is the first tar representative component that reaches a concentration of 1 mg.Nm -3 at a temperature of 325.15 K. On the other hand, toluene is the last tar representative component reaching 1 mg.Nm -3 at 196.15 K. Finally, the temperatures for indene, naphthalene and phenol, required to reach a concentration of 1 mg.Nm -3 , are 237.15 K, 245.15 K and 240.15 K, respectively. As a conclusion, toluene is the last component reduced to a concentration of 1 mg.Nm -3 since it requires the lowest temperature.


Bilel Tarchoun
email: tarchoun.bilel@yahoo.com

Ihsen Alouani
email: ihsen.alouani@uphf.fr

Anouar Ben Khalifa

Mohamed Ali Mahjoub
email: §mohamedali.mahjoub@eniso.rnu.tn

Adversarial Attacks in a Multi-view Setting: An Empirical Study of the Adversarial Patches Inter-view Transferability

Keywords: Adversarial Attacks, Perspective Geometric Transformations, View Angles, Adversarial patches, Multi-View

While machine learning applications are getting mainstream owing to a demonstrated efficiency in solving complex problems, they suffer from inherent vulnerability to adversarial attacks. Adversarial attacks consist of additive noise to an input which can fool a detector. Recently, successful real-world printable adversarial "patches" were proven efficient against state-of-the-art neural networks. In the transition from digital noise based attacks to real-world physical attacks, the myriad of factors affecting object detection will also affect adversarial patches. Among these factors, view angle is one of the most influential, yet under-explored. In this paper, we study the effect of view angle on the effectiveness of an adversarial patch. To this aim, we propose the first approach that considers a multiview context by combining existing adversarial patches with a perspective geometric transformation in order to simulate the effect of view angle changes. Our approach has been evaluated on two datasets: the first dataset which contains most real world constraints of a multi-view context, and the second dataset which empirically isolates the effect of view angle. The experiments show that view angle significantly affects the performance of adversarial patches, where in some cases the patch loses most of its effectiveness. We believe that these results motivate taking into account the effect of view angles in future adversarial attacks, and open up new opportunities for adversarial defenses.

I. INTRODUCTION

Artificial intelligence and machine learning are getting mainstream owing to their efficient problem-solving, and motivated by the need for automatically analyze increasingly larger amounts of data. More specifically, deep neural networks have shown impressive results in image classification and object detection tasks, with many architectures such as Faster R-CNN [START_REF] Ren | Faster r-cnn: Towards real-time object detection with region proposal networks[END_REF] and YOLO [START_REF] Redmon | You only look once: Unified, real-time object detection[END_REF] achieving high accuracy and/or real time processing speeds [START_REF] Tarchoun | Hand-crafted features vs deep learning for pedestrian detection in moving camera[END_REF]. However, with the increased popularity came increasing scrutiny into the safety and reliability of the results against possible tampering. These suspicions were confirmed by Szegedy [START_REF] Szegedy | Intriguing properties of neural networks[END_REF] and Goodfellow [START_REF] Goodfellow | Explaining and harnessing adversarial examples[END_REF] who were able to manipulate the results of deep neural networks with nearly imperceptible adversarial noise. In the hands of a malicious actor, these attacks can be used to fool neural networks, raising security concerns in safety critical applications such as surveillance cameras and selfdriving vehicles [START_REF] Ben Khalifa | A novel multi-view pedestrian detection database for collaborative intelligent transportation systems[END_REF], [START_REF] Tarchoun | Deep cnn-based pedestrian detection for intelligent infrastructure[END_REF].

Recent work involving adversarial attacks is focusing on applying these attacks in real-world scenarios, and finding defenses against such threats. Since adding noise to an image in real world conditions is impractical, current research is trending towards physical "adversarial patches" [START_REF] Thys | Fooling automated surveillance cameras: Adversarial patches to attack person detection[END_REF], [START_REF] Liu | Dpatch: An adversarial patch attack on object detectors[END_REF], [START_REF] Karmon | LaVAN: Localized and visible adversarial noise[END_REF], that can fool detectors and are easy to add to existing objects such as clothing or road signs. Compared to laboratory settings, several factors increase the difficulty of attacking a detector, such as illumination variations, distance from the obstacle and view angles [START_REF] Liu | Deep learning for generic object detection: A survey[END_REF]. Moreover, several applications of object detection such as surveillance cameras [START_REF] Chavdarova | Wildtrack: A multi-camera hd dataset for dense unscripted pedestrian detection[END_REF] and collaborative transportation systems [START_REF] Ben Khalifa | A novel multi-view pedestrian detection database for collaborative intelligent transportation systems[END_REF] are using multi-view camera systems. With such settings, the current state-of-the-art [START_REF] Hou | Multiview detection with feature perspective transformation[END_REF], [START_REF] López-Cifuentes | Semantic driven multi-camera pedestrian detection[END_REF] is reaching impressive results even on the most challenging of multi-view datasets [START_REF] Ferryman | Pets2009: Dataset and challenge[END_REF].

Although multi-view settings are showing very promising results, very few adversarial attacks consider its effect on their proposed attacks. In fact, patch testing on current stateof-the-art multi-view datasets is still limited to each isolated view. To the best of our knowledge, there is no extensive study in the literature that investigates the impact of multiview on real-life adversarial attacks.

In this paper, we study the impact of angle on the effectiveness of adversarial patch attacks. Specifically, we undertake an empirical characterization by generating a patch and applying a perspective geometric transformation to simulate the effects of view angles on the patch. To obtain accurate results, we evaluate our approach on a real-world multi-view dataset, we also introduce a new dataset where the effect of view angle is isolated as much as possible.

The contributions of our paper can be summarized as follows:

• We investigate the effect of view-angle on adversarial patches efficiency using geometric transformations. • We empirically evaluate different patches on both our proposed dataset and a multi-view dataset in real-world conditions.

Section 2 of our paper presents our attack approach and setting, Section 3 showcases our experiments and results and discusses the implications of angle on attack effectiveness and we conclude in Section 4.

II. PROPOSED METHODOLOGY

The aim of our paper is to study the effect of angle on adversarial patches effectiveness. For this goal, we propose an attack setting in which we train an adversarial patch on a multi-view pedestrian detection dataset, then apply the patch to only a single view of the dataset. In the next step, we use perspective geometric transformations to infer the patch on the different views. Finally, we evaluate the patch's effectiveness by applying a pedestrian detector to the new dataset obtained by applying the patch and subsequent geometric transformations. Figure 1 shows a diagram of our method.

In this experiment, we use the attack generation method proposed in [START_REF] Thys | Fooling automated surveillance cameras: Adversarial patches to attack person detection[END_REF], as it has shown good performance especially when applied to a objects with high intra-class variations such as persons. The goal of this attack is to generate a patch that hides people from the detector by minimizing the objectness score of the person to hide in the context of surveillance camera systems. The patch is created by optimizing the pixels in the patch area using an Adam optimizer that minimizes the following loss function shown in equation 1:

L = αL nps + βL tv + γL obj (1)
Where α, β and γ are empirically determined scaling factors, L nps is a non-printability score that ensures that the colors in the patch are printable by common printers, L tv symbolizes the difference in colors between neighboring pixels, and emphasizes smoother patches, and L obj is the maximum objectness score in the image. This is the main component in the loss function, as the lower this score is, the less likely the detector is able to detect the person.

After generating the patch, we apply it to a single view of the dataset (henceforth referred to as the "reference view"). Then, we apply a perspective geometric transformation (P) between each frame of the reference view and its corresponding frame in other views using the geometric transformation described in Equation 2:

  x dst y dst 1   =   p 11 p 12 p 13 p 21 p 22 p 23 p 31 p 32 1     x ref y ref 1   (2)
Where (x ref , y ref ) refers to a pixel in the reference view, (x dst , y dst ) refers to a pixel in the destination view, (p 11 , p 12 , p 21 , p 22 ) form the rotation matrix and are composed of various transformations to the image (Scale, shear and rotation), p 31 and p 32 are the translation vector, and p 13 and p 23 form the tilt vector. Obtaining the parameters of the geometric transformation can be likened to solving the linear system shown in equation 3, requiring at least four correspondence points that can be obtained using calibration data or manually extracted.



Finally, we obtain a new dataset with an adversarial patch applied to the reference view and transferred to other views via perspective geometric transformations. We can now apply the chosen pedestrian detector to the dataset we have prepared.

III. EXPERIMENTS AND RESULTS

In this section we apply then aforementioned method in the previous section and discuss the obtained results:

A. Testing Parameters

In order to generate the patch, we use the following empirical hyperparameters shown in Table I. All trained patches use random values as a starting point. To ensure proper testing conditions, we choose YOLOv2 for pedestrian detection, as it was the target detector of the chosen adversarial patch. Furthermore, we use pre-trained weights for the YOLOv2 detector that are provided by the authors of the patch.

B. Wildtrack tests and results

We first test our patch on the Wildtrack multiview dataset [START_REF] Chavdarova | Wildtrack: A multi-camera hd dataset for dense unscripted pedestrian detection[END_REF]. We generate our patch on view 1, and we divide the views into two sets {1, 4, 6, 7} and {2, 3, 5} since we add a patch to each visible person's front and back. We choose views 1 and 5 as reference views since those cameras are opposed to each other. Figure 2 shows the patch added in a sample frame of view 7 after the geometric transformation step. The results of these experiments are shown in tables II and III below. The recall values are the percentage of people correctly detected among the number of people that are present in both the reference view and destination view since we do not take into account people who are only visible in only one of the views.

C. LATIS-MVAPE database tests and results

To better isolate the effects of view angle, we create our own dataset: LATIS-Multi-View Adversarial Patch Evaluation (LATIS-MVAPE) which contains 14 images of 18 different persons taken from seven different view angles and two distances for a total of 252 photos. 11 persons were filmed in indoor conditions, and the other seven were filmed outdoors. For the geometric transformation correspondence points, we manually select 18 pairs of points between the reference view and each destination view. We train 2 patches: The first patch with only the reference view as input data, and the second patch with the reference and 60 • views from the dataset as input data. Figure 3 shows a sample of patch application with the subsequent geometric transformation in both the reference view and in the 60 • view respectively. Table IV below shows the detection results with each of the applied patches:

D. Discussion

Our results confirm that angle has a significant effect on the effectiveness of an adversarial patch attack: In Wildtrack database experiments, the projected patch lost up to half its efficiency compared to the one applied to the reference view. In the tests performed on our database, the projected patch was at best half as effective as the reference view patch, losing nearly all of its attack capabilities in some cases.

These findings have the potential to impact both the attacking side and the defending side in adversarial patch attacks: On the attack side, this study highlights the need to incorporate view angle into the patch creation process, as the patch loses a significant amount of its effectiveness when viewed from an angle. This integration can be achieved by adding geometric transformations in the patch training process, or including multi-view data in the training dataset.

As for the defense side, these findings suggest interesting pathways to stronger defenses, such as adopting multi-view detection techniques. These multi-view detectors combine data from all of the available views to locate objects within them, and as such can detect objects in a certain view that a monocular detector failed to detect using the extra multiview data. Therefore, to attack such a potential defense, an adversary has to defeat the detector on all of the views, reducing the strength of the adversarial patch to be only as strong as the weakest view, and in the case of a patch untrained against view angle variations, this effectiveness loss can be severe.

IV. CONCLUSION

The threat of adversarial attacks has spurred increasing interest in designing practical adversarial attacks and defenses in the real world. To better understand these attacks, an investigation of the interactions between adversarial patches and the factors that affect object detection is necessary. This paper unprecedentedly studies the effect of view angle on the effectiveness of an adversarial patch. To emulate the angle view deformation on the patch, we apply a perspective geometric transformation to an existing attack. We compared the original patch results with the transformed patch on other views, and we notice a significant effect on attack success rates. This difference has implications for adversarial patches on both tasks of attacking a detector or immunizing detectors from such attacks.

Figure 1 .

 1 Figure 1. Diagram of the proposed exploration method

Figure 3 .

 3 Figure 3. Sample of a successful adversarial attack on the reference view of LATIS-MVAPE, while it fails to fool the detector at a different angle.

  Sample of the Wildtrack view 7 detection results with the patch applied after geometric transformation Table III PATCH PERFORMANCE ON THE {2,3,5} VIEW SET

				Figure 2. View	Clean Recall Patched Recall Difference (%)
				5 (ref)	43.37	9.72	-77.59%
				2	22.97	20.85	-9.23%
				3	24.59	10.28	-58.19%
			Table II
	PATCH PERFORMANCE ON THE {1,4,6,7} VIEW SET
	View	Clean Recall Patched Recall Difference (%)
	1 (ref)	17.35	1.04	-94.01%
	4	30.28	15.86	-47.62%
	6	7.56	4.03	-46.69%
	7	44.22	6.97	-84.24%