  Many defenses have recently been proposed at venues like NIPS, ICML, ICLR and
CVPR. These defenses are mainly focused on mitigating white-box attacks. They
do not properly examine black-box attacks. In this paper, we expand upon the
analysis of these defenses to include adaptive black-box adversaries. Our
evaluation is done on nine defenses including Barrage of Random Transforms,
ComDefend, Ensemble Diversity, Feature Distillation, The Odds are Odd, Error
Correcting Codes, Distribution Classifier Defense, K-Winner Take All and Buffer
Zones. Our investigation is done using two black-box adversarial models and six
widely studied adversarial attacks for CIFAR-10 and Fashion-MNIST datasets. Our
analyses show most recent defenses (7 out of 9) provide only marginal
improvements in security ($<25\%$), as compared to undefended networks. For
every defense, we also show the relationship between the amount of data the
adversary has at their disposal, and the effectiveness of adaptive black-box
attacks. Overall, our results paint a clear picture: defenses need both
thorough white-box and black-box analyses to be considered secure. We provide
this large scale study and analyses to motivate the field to move towards the
development of more robust black-box defenses.
