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Many defenses have recently been proposed at venues like NIPS, ICML, ICLR andCVPR. These defenses are mainly focused on mitigating white-box attacks. Theydo not properly examine black-box attacks. In this paper, we expand upon theanalysis of these defenses to include adaptive black-box adversaries. Ourevaluation is done on nine defenses including Barrage of Random Transforms,ComDefend, Ensemble Diversity, Feature Distillation, The Odds are Odd, ErrorCorrecting Codes, Distribution Classifier Defense, K-Winner Take All and BufferZones. Our investigation is done using two black-box adversarial models and sixwidely studied adversarial attacks for CIFAR-10 and Fashion-MNIST datasets. Ouranalyses show most recent defenses (7 out of 9) provide only marginalimprovements in security ($<25\%$), as compared to undefended networks. Forevery defense, we also show the relationship between the amount of data theadversary has at their disposal, and the effectiveness of adaptive black-boxattacks. Overall, our results paint a clear picture: defenses need boththorough white-box and black-box analyses to be considered secure. We providethis large scale study and analyses to motivate the field to move towards thedevelopment of more robust black-box defenses.

Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples