Sign Bits Are All You Need for Black-Box Attacks

1 INTRODUCTION . Problem . Deep Neural Networks ( DNNs ) are vulnerable to adversarial examples , which are malicious inputs designed to fool the model ’ s prediction—see ( Biggio and Roli , 2018 ) for a comprehensive , recent overview of adversarial examples . Research on generating these malicious inputs started in the white-box setting , where access to the gradients of the models is assumed . Since the gradient points to the direction of steepest ascent , an input can be perturbed along the gradient ’ s direction to maximize the network ’ s loss , thereby potentially causing misclassification under class prediction , e.g . with images , or evasion under detection , e.g . with malware . The assumption of access to the underlying gradient does not however reflect real world scenarios . Attack algorithms under a more realistic , restrictive black-box threat model , which assumes access to predictions in lieu of gradients , are therefore studied . Central to their approaches is estimating the gradient . To estimate the magnitudes and signs of the gradient , the community at large has formulated a continuous optimization problem of O ( n ) complexity where n is the input dimensionality . Most recently work has sought to reduce this complexity by means of data-/time-dependent priors Ilyas et al . ( 2019 ) . In this paper , we take a different tact and reduce the central problem to just estimating the signs of the gradients . Our intuition arises from observing that estimating the sign of the top 30 % gradient coordinates by magnitude is enough to achieve a rough misclassification rate of 70 % . Figure 1 reproducing Ilyas et al . ( 2019 ) illustrates this observation for the MNIST dataset–see Appendix A for other datasets . Therefore our goal is to recover the sign of the gradient with high query efficiency so we can use it to generate adversarial examples as effective as those generated by full gradient estimation approaches . Related Work . We organize the related work in two themes , namely Adversarial Example Generation and Sign-Based Optimization . The literature of the first theme primarily divides into white-box and black-box settings . The white-box setting , while not the focus of this work , follows from the works of Biggio et al . ( 2013 ) and Goodfellow et al . ( 2015 ) who introduced the Fast Gradient Sign Method ( FGSM ) , including several methods to produce adversarial examples for various learning tasks and threat perturbation constraints ( Carlini and Wagner , 2017 ; Moosavi-Dezfooli et al. , 2016 ; Hayes and Danezis , 2017 ; Al-Dujaili et al. , 2018 ; Kurakin et al. , 2017 ; Shamir et al. , 2019 ) . Turning to the blackbox setting and iterative optimization schemes , Narodytska and Kasiviswanathan ( 2017 ) , without using any gradient information , use a naive policy of perturbing random segments of an image to generate adversarial examples . Bhagoji et al . ( 2017 ) reduce the dimensions of the feature space using Principal Component Analysis ( PCA ) and random feature grouping , before estimating gradients . Chen et al . ( 2017 ) introduce a principled approach by using gradient based optimization . They employ finite differences , a zeroth-order optimization means , to estimate the gradient and then use it to design a gradient-based attack . While this approach successfully generates adversarial examples , it is expensive in how many times the model is queried . Ilyas et al . ( 2018 ) substitute traditional finite differences methods with Natural Evolutionary Strategies ( NES ) to obtain an estimate of the gradient . Tu et al . ( 2018 ) provide an adaptive random gradient estimation algorithm that balances query counts and distortion , and introduces a trained auto-encoder to achieve attack acceleration . Ilyas et al . ( 2019 ) extend this line of work by proposing the idea of gradient priors and bandits : BanditsTD . Our work contrasts with the general approach of these works in two ways : a ) We focus on estimating the sign of the gradient and investigate whether this estimation suffices to efficiently generate adversarial examples . b ) The above methods employ random sampling in constructing queries to the model while our construction is adaptive.1 Another approach involves learning adversarial examples for one model ( with access to its gradient information ) to transfer them against another ( Liu et al. , 2016 ; Papernot et al. , 2017 ) . Alternately , Xiao et al . ( 2018 ) use a Generative Adversarial Network ( GAN ) to generate adversarial examples which are based on small norm-bounded perturbations . These methods involve learning on a different model , which is expensive , and not amenable to comparison with setups—including ours—that directly query the model of interest . Sign-Based Optimization . In the context of generalpurpose continuous optimization methods , signbased stochastic gradient descent was studied in both zeroth- and first-order setups . In the latter , Bernstein et al . ( 2018 ) analyzed signSGD , a sign-based Stochastic Gradient Descent , and showed that it enjoys a faster empirical convergence than SGD in addition to the cost reduction of communicating gradients across multiple workers . Liu et al . ( 2019 ) extended signSGD to zeroth-order setup with the ZO-SignSGD algorithm . ZO-SignSGD ( Liu et al. , 2019 ) was shown to outperform NES against a blackbox model on MNIST . These approaches use the sign of the gradient ( or its zero-order estimate ) to achieve better convergence , whereas our approach both estimates and uses the sign of the gradient . Contributions . We present the following contributions at the intersection of adversarial machine learning and black-box ( zeroth-order ) optimization : 1 ) We exploit the separability property of the directional derivative of the loss function of the model under attack in the direction of { ±1 } n vectors , to propose a divide-and-conquer , adaptive , memory-efficient algorithm , we name SignHunter , to estimate the gradient sign bits . 2 ) We provide a worst-case theoretical guarantee on the number of queries required by SignHunter to perform at least as well as FGSM ( Goodfellow et al. , 2015 ) , which has access to the model ’ s gradient . To our knowledge , no black-box attack from the literature offers a similar performance guarantee . 3 ) We evaluate our approach on a rigorous set of experiments on both , standard and adversarially hardened models . All other previous works on this topic have published their results on a subset of the datasets and threat models we experimentally validate in this work . Through these experiments , we demonstrate that SignHunter ’ s adaptive search for the gradient sign allows it to craft adversarial examples within a mere fraction of the theoretical number of queries thus outperforming FGSM and state-of-the-art black-box attacks . 4 ) We release a software framework to systematically benchmark adversarial 1We use the term adaptive here to characterize how the algorithm constructs its perturbation vector qt deterministically based on the previous perturbations . Mathematically , the perturbation vector at time t can be expressed as deterministic function qt = g ( qt−1 ) . In contrast , other algorithms construct I.I.D randomly perturbation vectors : qt ∼ N ( 0 , I ) . black-box attacks , including SignHunter ’ s , on MNIST , CIFAR10 , and IMAGENET models in terms of success rate , query count , and other metrics . 5 ) We demonstrate how SignHunter can be used to characterize adversarial cones in a black-box setup and in doing so , highlight the gradient masking effect . Notation . Let n denote the dimension of datapoint x. Denote a hidden n-dimensional binary code by q∗ . That is , q∗ ∈ H ≡ { −1 , +1 } n. Further , denote the directional derivative of some function f at a point x in the direction of a vector v by Dvf ( x ) ≡ vT∇xf ( x ) which often can be approximated by the finite difference method . That is , for δ > 0 , we have Dvf ( x ) = v T∇xf ( x ) ≈ f ( x+ δv ) − f ( x ) δ . ( 1 ) Let ΠS ( · ) be the projection operator onto the set S , Bp ( x , ) be the ` p ball of radius around x . 2 GRADIENT ESTIMATION . At the heart of black-box adversarial attacks is generating a perturbation vector to slightly modify the original input x so as to fool the network prediction of its true label y . Put differently , an adversarial example x′ maximizes the network ’ s loss L ( x′ , y ) but still remains -close to the original input x . Although the loss function L can be non-concave , gradient-based techniques are often very successful in crafting an adversarial example Madry et al . ( 2017 ) . That is , setting the perturbation vector as a step in the direction of ∇xL ( x , y ) . Consequently , the bulk of black-box attack methods try to estimate the gradient by querying an oracle that returns , for a given input/label pair ( x , y ) , the value of the network ’ s loss L ( x , y ) , consulting prediction or classification accuracy . Using only such value queries , the basic approach relies on the finite difference method to approximate the directional derivative ( Eq . 1 ) of the function L at the input/label pair ( x , y ) in the direction of a vector v , which corresponds to vT∇xL ( x , y ) . With n linearly independent vectors { viT∇xL ( x , y ) = di } 1≤i≤n , one can construct a linear system of equations to recover the full gradient . Clearly , this approach ’ s query complexity is O ( n ) , which can be prohibitively expensive for large n ( e.g. , n = 268 , 203 for the IMAGENET dataset ) . Recent works try to mitigate this issue by exploiting data- and/or time-dependent priors ( Tu et al. , 2018 ; Ilyas et al. , 2018 ; 2019 ) . However , the queries are not adaptive , they are constructed based on i.i.d . random vectors { vi } . They fail to make use of the past queries ’ responses to construct the new query and recover the full gradient more efficiently . As stated in the introduction , we solve the smaller problem of gradient sign estimation with adaptive queries based on the observation that simply leveraging ( noisy ) sign bits of the gradient yields successful attacks–see Figure 1 . Definition 1 . ( Gradient Sign Estimation Problem ) For an input/label pair ( x , y ) and a loss function L , let g∗ = ∇xL ( x , y ) be the gradient of L at ( x , y ) and q∗ = sign ( g∗ ) ∈ H be the sign bit vector of g∗.2 Then the goal of the gradient sign estimation problem is to find a binary vector q ∈ H maximizing the directional derivative3 max q∈H DqL ( x , y ) , ( 2 ) from a limited number of ( possibly adaptive ) function value queries L ( x′ , y ) . 3 A METHOD FOR ESTIMATING SIGN OF THE GRADIENT FROM ADAPTIVE QUERIES . Our goal is to estimate the gradient sign bits of the loss function L of the model under attack at an input/label pair ( x , y ) from a limited number of loss value adaptive queries L ( x′ , y ) . To this end , we examine the basic concept of directional derivatives that has been employed in recent black-box 2Without loss of generality , we encode the sign bit vector in H ≡ { −1 , +1 } n rather than { 0 , 1 } n. This is a common representation in sign-related literature . Note that the standard sign function has the range of { −1 , 0 , +1 } . Here , we use the non-standard definition ( Zhao , 2018 ) whose range is { −1 , +1 } . This follows from the observation that DNNs ’ gradients with respect to their inputs are not sparse ( Ilyas et al. , 2019 , Appendix B.1 ) . 3The maximization follows from DqL ( x , y ) = qTg∗ , which is maximized when q = q∗ = sign ( g∗ ) . adversarial attacks . Based on the definition of the directional derivative ( Eq . 1 ) , the following can be stated . Property 1 ( Separability of DqL ( x , y ) ) . The directional derivative DqL ( x , y ) of the loss function L at an input/label pair ( x , y ) in the direction of a binary code q is separable . That is , max q∈H DqL ( x , y ) = max q∈H qTg∗ = n∑ i=1 max qi∈ { −1 , +1 } qig ∗ i . ( 3 ) Algorithm 1 SignHunter g : H → R : the black-box function to be maximized over the binary hypercubeH ≡ { −1 , +1 } n def init ( g ) : i← 0 , h← 0 g ← g s ∼ U ( H ) // e.g. , [ +1 , . . . , +1 ] done← false gbest ← −∞ def is_done ( ) : return done def step ( ) : c_len← dn/2he s [ i * c_len : ( i+1 ) * c_len ] * = -1 if g ( s ) ≥ gbest : gbest ← g ( s ) else : s [ i * c_len : ( i+1 ) * c_len ] * = -1 increment i if i == 2h : i← 0 , increment h if h == dlog2 ( n ) e+ 1 : done← true def get_current_sign_estimate ( ) : return s This reformulates the gradient sign estimation problem from single n-dimensional to n 1-dimensional binary black-box optimization problems , reducing the search space of sign bits from 2n to 2n . Subsequently , one could recover the gradient sign bits with n + 2 queries as follows : i . Start with an arbitrary sign vector q and compute the directional derivative DqL ( x , y ) . Using Eq . 1 , this requires two queries : L ( x+δq , y ) and L ( x , y ) . ii . For the remaining n queries , flip q ’ s bits ( coordinates ) one by one and compute the corresponding directional derivative– one query each L ( x+ δq , y ) . iii . Retain bit flips that maximize the directional derivative DqL ( x , y ) and revert those otherwise . This , however , still suffers from the O ( n ) complexity of full gradient estimation methods . Further , each query recovers at most one sign bit and the natural question to ask is : can we recover more sign bits per query ? Consider the case where all the gradient coordinates have the same magnitude , i.e. , | { |g∗i | } 1≤i≤n|= 1 , and let the initial guess q1 have r correct bits and n− r wrong ones . Instead of flipping its bits sequentially , we can flip them all at once to get q2 = −q1 . If Dq2L ( x , y ) ≥ Dq1L ( x , y ) , then we retain q2 as our best guess with n − r correct bits , otherwise q1 remains . In either cases , with three queries , we will recover max ( r , n− r ) sign bits . One can think of this flip/revert procedure as one of majority voting by the guess ’ s coordinates on whether they agree with their gradient sign ’ s counterparts . To see this , let |g∗i |= 1 for all i , then the condition Dq2L ( x , y ) ≥ Dq1L ( x , y ) can be written as n − r − r ≥ r− n+ r =⇒ n ≥ 2r . If the agree votes r are less than half of the total votes n , then q2 is retained . Besides flipping all the coordinates , one can employ the same procedure iteratively on a subset ( chunk ) of the coordinates [ qj , . . . , qj+ni ] of the guess vector q , recovering max ( ri , ni − ri ) sign bits , where ni and ri is the length of the ith chunk and the number of its correct signs , respectively . While the magnitudes of gradient coordinates may not have the same value as assumed in the previous example ; through empirical evaluation ( see Appendix F ) , we found them to be concentrated . Consequently and with high probability , their votes on retaining or reverting chunks of sign flips are weighted ( by their corresponding gradient magnitude ) similarly . That said , if we are at a chunk where the distribution of the gradient coordinate magnitudes is uniform , then the flip/revert procedure could favor recovering few sign coordinates with large magnitude counterparts over many sign coordinates with small magnitude counterparts . From our experiments on the noisy FGSM , this still suffices to generate adversarial examples : an attack with 30 % correct sign bits ( that correspond to the top gradient coordinates magnitudes ) is more effective than an attack with 50 % correct arbitrary sign bits as shown in Figure 1 . Put differently , we would like to recover as many sign bits as possible with as few queries as possible . However , if we can only recover few , they should be those that correspond to coordinates with large gradient magnitude . This notion is in line with the flip/revert procedure . We employ the above observation in a divide-and-conquer search which we refer to as SignHunter . As outlined in Algorithm 1 , the technique starts with an initial guess of the sign vector q1 ( s in Algorithm 1 ) . It then proceeds to flip the sign of all the coordinates to get a new sign vector q2 , and revert the flips if the loss oracle returned a value L ( x+ δq2 , y ) ( or equivalently the directional derivative ) less than the best obtained so far L ( x+ δq1 , y ) . SignHunter applies the same rule to the first half of the coordinates , the second half , the first quadrant , the second quadrant , and so on . For a search space of dimension n , SignHunter needs 2dlog ( n ) +1e − 1 sign flips to complete its search . If the query budget is not exhausted by then , one can update x with the recovered signs and restart the procedure at the updated point with a new starting code s. If we start with a sign vector whose Hamming distance to the optimal sign vector q∗ is n/2 : agreeing with q∗ in the first half of coordinates . In this case , SignHunter needs just four queries to recover the entire sign vector independent of n , whereas the sequential bit flipping still require n+ 2 queries . In the next theorem , we show that SignHunter is guaranteed to perform at least as well as FGSM with O ( n ) oracle queries . Up to our knowledge , no such guarantees exist for any black-box attack from the literature . Theorem 1 . ( Optimality of SignHunter ) Given 2dlog ( n ) +1e queries and that the directional derivative is well approximated by the finite-difference ( Eq . 1 ) , SignHunter is at least as effective as FGSM ( Goodfellow et al. , 2015 ) in crafting adversarial examples . The proof can be found in Appendix B. Theorem 1 provides an upper bound on the number of queries required for SignHunter to recover the gradient sign bits , and perform as well as FGSM . In practice ( as will be shown in our experiments ) , SignHunter crafts adversarial examples with a small fraction of this upper bound . The rationale here is that we do not need to recover the sign bits exactly ; we rather need a fast convergence to an adversarially helpful sign vector s. In our setup , we use the best sign estimation obtained s so far in a similar fashion to FGSM , whereas full-gradient estimation approaches often employ an iterative scheme of T steps within the perturbation ball Bp ( x , ) , calling the gradient estimation routine in every step leading to a search complexity of nT . Instead , our gradient sign estimation routine runs at the top level of our adversarial example generation procedure . Further , SignHunter is amenable to parallel hardware architecture and has a smaller memory footprint ( just sign bits ) and thus can carry out attacks in batches more efficiently . Crafting black-box adversarial attacks with SignHunter is outlined in Algorithm 2 . 4 EXPERIMENTS Algorithm 2 Black-Box Adversarial Example Generation with SignHunter xinit : input to be perturbed , yinit : xinit ’ s true label , Bp ( . , ) : ` p perturbation ball of radius L : loss function of the model under attack 1 : δ ← // set finite-difference probe to perturbation bound 2 : xo ← xinit 3 : Define the function g as g ( q ) = L ( ΠBp ( xinit , ) ( xo + δq ) , yinit ) − L ( xo , yinit ) δ 4 : SignHunter.init ( g ) 5 : //C ( · ) returns top class 6 : while C ( x ) = yinit do 7 : SignHunter.step ( ) 8 : s← SignHunter.get_current_sign_estimate ( ) 9 : x← ΠBp ( xinit , ) ( xo + δs ) 10 : if SignHunter.is_done ( ) then 11 : xo ← x 12 : Define the function g as in Line 3 ( with xo update ) 13 : SignHunter.init ( g ) 14 : return x We evaluate SignHunter and compare it with established algorithms from the literature : ZO-SignSGD Liu et al . ( 2019 ) , NES Ilyas et al . ( 2018 ) , and BanditsTD Ilyas et al . ( 2019 ) in terms of effectiveness in crafting ( without loss of generality ) untargeted black-box adversarial examples . To highlight SignHunter ’ s adaptive query construction , we introduce a variant of Algorithm 2 , named Rand . At every iteration , Rand ’ s sign vector is sampled uniformly from H.4 . Both ` ∞ and ` 2 threat models are considered on the MNIST , CIFAR10 , and IMAGENET datasets . Code and data for the experiments can be found at https : //bit.ly/3acIHoQ . Experiments Setup . Our experiment setup is similar to ( Ilyas et al. , 2019 ) . Each attacker is given a budget of 10 , 000 oracle queries per attack attempt and is evaluated on 1000 images from the test sets of MNIST , CIFAR10 , and the validation set of IMAGENET . We did not find a standard practice for setting the perturbation bound . For the ` ∞ threat model , we use ( Madry et al. , 2017 ) ’ s bound for MNIST and ( Ilyas et al. , 2019 ) ’ s bounds for both CIFAR10 and IMAGENET . For the ` 2 threat model , ( Ilyas et al. , 2019 ) ’ s bound is used for IMAGENET . MNIST ’ s bound is set based on the sufficient distortions observed in ( Liu et al. , 4That is , replace Line 8 in Algorithm 2 by s ∼ U ( H ) . 2019 ) , which are smaller than the one used in ( Madry et al. , 2017 ) . We use the observed bound in ( Cohen et al. , 2019 ) for CIFAR10 . We show results based on standard models–i.e. , models that are not adversarially hardened . For MNIST and CIFAR10 , the naturally trained models from ( Madry et al. , 2017 ) ’ s MNIST and CIFAR10 challenges are used . For IMAGENET , TensorFlow ’ s Inception ( v3 ) model is used . The loss oracle returns the cross-entropy loss of the respective model . See Appendix C for other general experimental setup details . Hyperparameters Setup . While SignHunter does not have any hyperparameters , to fairly compare it with the other algorithms , we tuned their hyperparameters starting with the default values reported by the corresponding authors . The finite difference probe δ for SignHunter is set to the perturbation bound as it is used for both computing the finite difference and crafting the adversarial examples— see Line 1 in Algorithm 2 . This tuning-free aspect of SignHunter offers a robustness advantage over algorithms which require expert hypertuning . Details on the hyperparameter setup are available in Appendix C. Results . Figure 2 shows the trade-off between the success ( evasion ) rate and the mean number of queries ( of the successful attacks , per convention ) needed to generate an adversarial example for the MNIST , CIFAR10 , and IMAGENET classifiers under the ` ∞ and ` 2 perturbation constraints . These plots indicate the average number of queries required for a desired success rate . Table 1 represents a tabulated summary of plots ( b ) and ( e ) of Figure 2.5 We observe the following : For any given success rate , SignHunter dominates the previous state of the art approaches in all settings except the IMAGENET ` 2 setup , where BanditsTD shows a better query efficiency when the desired success rate is roughly greater than 0.35 . This is all the more remarkable because BanditsTD 5More tabulated summaries of these plots can be found in Appendix D–Tables 7–9 . We also plot the classifier ’ s loss and the gradient estimation quality ( in terms of Hamming distance and cosine similarity ) averaged over all the images as a function of the number of queries in Figures 4–6 of Appendix D. exploits tiles , a data-dependent prior , searching over 50× 50× 3 dimensions for IMAGENET , while SignHunter searches over the explicit data 299× 299× 3 dimensions : 36× more dimensions . ` ∞ vs. ` 2 Perturbation Threat . In view of BanditsTD ’ s advantage , SignHunter is remarkably efficient in the ` ∞ setup , achieving a 100 % evasion using—on average—just 12 queries per image against the MNIST classifier ! In the ` 2 setup , SignHunter ’ s performance degrades—yet it still outperforms the other algorithms . This is expected , since SignHunter perturbs all the coordinates with the same magnitude and the ` 2 perturbation bound 2 for all the datasets in our experiments is set such that 2/ √ n is significantly less than the ` ∞ perturbation bound ∞ . Take the case of MNIST ( n = 28× 28 ) , where ∞ = 0.3 and 2 = 3 . For SignHunter , the ` 2 setup is equivalent to an ` ∞ perturbation bound of 3/28 ≈ 0.1 . The employed ` 2 perturbation bounds give the state of the art—continuous optimization based—approaches more perturbation options . For instance , it is possible for NES to perturb just one pixel in an MNIST image by a magnitude of 3 ; two pixels by a magnitude of 3/ √ 2 ≈ 2.1 each ; ten pixels by a magnitude of 3/ √ 10 ≈ 0.9 each , etc . On the other hand , the binary optimization view of SignHunter limits it to always perturb all 28× 28 pixels by a magnitude of 3/28 ≈ 0.1 . Despite its fewer degrees of freedom , SignHunter maintains its effectiveness in the ` 2 setup . The plots can also be interpreted as a sensitivity assessment of SignHunter as gets smaller going from ` ∞ to the ` 2 perturbation threat . SignHunter vs FGSM . The performance of SignHunter is in line with Theorem 1 when compared with the performance of FGSM ( the noisy FGSM at k = 100 % in Figures 1 and 2 of Appendix A ) in both ` ∞ and ` 2 setups across all datasets . For instance , FGSM has a failure rate of 0.32 for CIFAR10 ` 2 ( Appendix A , Figure 2 ( b ) ) , while SignHunter achieves a failure rate of 0.21 with 692.39 < 2n = 2× 3× 32× 32 = 6144 queries ( Appendix D , Table 8 ) . Note that for IMAGENET , SignHunter outperforms FGSM with a query budget of 10 , 000 queries , a fraction of the theoretical number of queries required 2n = 536 , 406 to perform at least as well . Incorporating SignHunter in an iterative framework of perturbing the data point x till the query budget is exhausted ( Lines 10 to 14 in Algorithm 2 ) supports the observation in white-box settings that iterative FGSM—or Projected Gradient Descent ( PGD ) —is stronger than FGSM ( Madry et al. , 2017 ; Al-Dujaili et al. , 2018 ) . This is evident by the upticks in SignHunter ’ s performance on the MNIST ` 2 case ( Appendix D , Figure 4 ) , which happens after every iteration ( after every other 2× 28× 28 queries ) . Gradient Estimation . Plots of the Hamming similarity capture the number of recovered sign bits , while plots of the average cosine similarity capture the value of Eq . 2 . Both SignHunter and BanditsTD consistently optimize both metrics . In general , SignHunter ( BanditsTD ) converges faster especially on the Hamming ( cosine ) metric as it is estimating the signs ( signs and magnitudes ) compared to BanditsTD ’ s full gradient ( SignHunter ’ s gradient sign ) estimation . This is most obvious in the IMAGENET ` 2 setup ( Appendix D , Figure 6 ) . Note that once an attack is successful , the estimated gradient sign at that point is used for the rest of the plot . This explains why , in the ` ∞ settings , SignHunter ’ s plot does not improve compared to its ` 2 counterpart , as most of the attacks are successful in the very first few queries made to the loss oracle and no further refined estimation is required . Another possible reason is that the gradient direction can be very local and does not capture the global loss landscape compared to SignHunter ’ s estimation . More on this is discussed in Section 6 . SignHunter vs. Rand . Given these results , one could argue that SignHunter is effective , because it maximally perturbs datapoints to the vertices of their perturbation balls.6 However , Rand ’ s poor performance does not support this argument and highlights the effectiveness of SignHunter ’ s adaptive query construction . Except for MNIST and CIFAR10 ` ∞ settings , Rand performs worse than the full-gradient estimation approaches , although it perturbs datapoints similar to SignHunter . Overall , SignHunter is 3.8× less failure-prone than the state-of-the-art approaches combined , and spends over all the images ( successful and unsuccessful attacks ) 2.5× less queries.7 5 SIGNHUNTER VS . DEFENSES To complement Section 4 , we evaluate SignHunter against adversarial training , a way to improve the robustness of DNNs ( Madry et al. , 2017 ) . Specifically , we attacked the secret models used 6We define perturbation vertices as extreme points of the ball Bp ( x , ) . That is , x ± ∞ , where ∞ = when p =∞ and ∞ = / √ n when p = 2 . 7 The number of queries spent is computed based on Tables 7–9 of Appendix D as ( 1 - fail_rate ) * avg_ # _queries + fail_rate * 10,000. in public challenges for MNIST and CIFAR10 . For IMAGENET , we used ensemble adversarial training , a method that argues security against black-box attacks based on transferability Tramèr et al . ( 2017a ) . Appendix E reports the same metrics used in Section 4 as well as a tabulated summary for the results discussed below . Public MNIST Black-Box Attack Challenge . In line with the challenge setup , 10 , 000 test images were used with an ` ∞ perturbation bound of = 0.3 . Although the secret model is released , we treated it as a black box similar to our experiments in Section 4 . No maximum query budget was specified , so we set it to 5 , 000 queries . This is equal to the number of iterations given to a PGD attack in the white-box setup of the challenge : 100-steps with 50 random restarts . SignHunter ’ s attacks resulted in the lowest model accuracy of 91.47 % , outperforming all the submitted attacks to the challenge , with an average number of queries of 233 per successful attack . Note that the attacks submitted to the challenge are based on transferability and do not query the model of interest . On the other hand , the most powerful white-box attack by Wang et al . ( 2018 ) —as of May 15 , 2019—resulted in a model accuracy of 88.42 % . Further , a PGD attack with 5 , 000 back-propagations achieves 89.62 % in contrast to SignHunter ’ s 91.47 % with just 5 , 000 forward-propagations . Public CIFAR10 Black-Box Attack Challenge . This challenge setup is similar to the above , but with an ` ∞ perturbation bound of = 8 . SignHunter ’ s attacks resulted in the lowest model accuracy of 47.16 % , outperforming all the submitted attacks to the challenge , with an average number of queries of 569 per successful attack . Similar to the MNIST challenge , all the submitted attacks are based on transferability . On the other hand , the most powerful white-box attack by Zheng et al . ( 2018 ) —as of May 15 , 2019—resulted in a model accuracy of 44.71 % . Further , a PGD attack with 200 back-propagations achieves 45.21 % in contrast to SignHunter ’ s 47.16 % with 5 , 000 forward-propagations . Ensemble Adversarial Training on IMAGENET . In line with Tramèr et al . ( 2017a ) , we set = 0.0625 and report the v3adv-ens4 model ’ s misclassification over 10,000 random images from IMAGENET ’ s validation set . After 20 queries , SignHunter achieves a top-1 error of 40.61 % greater than the 33.4 % rate of a series of black-box attacks ( including PGD with 20 iterations ) transferred from a substitute model . With 1000 queries , SignHunter breaks the model ’ s robustness with a top-1 error of 90.75 % ! 6 CHARACTERIZING ADVERSARIAL CONES WITH SIGNHUNTER Estimating the size of adversarial cones , the space of adversarial examples in the vicinity of a point , for a model has been a topic of interest by the machine learning community Tramèr et al . ( 2017a ) ; Ma et al . ( 2018 ) ; Lu et al . ( 2018 ) . The Gradient-Aligned Adversarial Subspace ( GAAS ) method Tramèr et al . ( 2017b ) provides an approximation of the adversarial cone dimensionality by finding a set of orthogonal perturbations of norm that are all adversarial with respect to the model . By linearizing the model ’ s loss function , this is reduced to finding orthogonal vectors that are maximally aligned with its gradient g∗—or its gradient sign q∗ in the ` ∞ setup Tramèr et al . ( 2017a ) . In Figure 3 , we reproduce ( Tramèr et al. , 2017a , Fig . 2 ) and show that aligning the orthogonal vectors with SignHunter ’ s estimation ( we refer to this approach as SAAS ) instead of aligning them with the gradient ( GAAS ) results in a better approximation of the adversarial cone for the two IMAGENET models considered earlier , even when the number of queries given to SignHunter is just a fraction of the dimensionality n. Through its query-efficient finite-difference sign estimation , SignHunter is able to quickly capture the larger-scale variation of the loss landscape in the point ’ s neighborhood , rather than the infinitesimal point-wise variation that the gradient provides , which can be very local . This is important in adversarial settings , where the loss landscape is analyzed in the vicinity of the point Moosavi-Dezfooli et al . ( 2018 ) ; Tramèr et al . ( 2017a ) . One interesting observation at k = 1 ( note here , r1 = q∗ ) across all is that GAAS finds adversarial directions for fewer points against the v3adv-ens4 model than the naturally trained model v3 , whereas SAAS reports similar probability of adversarial directions for both . This contrast suggests that ensemble adversarial training Tramèr et al . ( 2017a ) still exhibits the gradient masking effect , where the gradient poorly approximates the global loss landscape .

Summary: In this paper, the authors introduce a black box adversarial attack based on estimating the sign of the gradient. To estimate this more efficiently than the gradient itself, the authors exploit the fact that directional derivatives can be estimated using finite differences using only two function evaluations, and use a simple flip/revert procedure to estimate a number of sign bits simultaneously. This sign bit gradient vector is then used in place of the true gradient in an FGSM-like procedure. The central arguments are that (1) estimating the sign bits alone is sufficient to produce adversarial examples, and (2) this can be done quickly enough in practice to yield an efficient procedure.