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Most adversarial attack defense methods rely on obfuscating gradients. Thesemethods are successful in defending against gradient-based attacks; however,they are easily circumvented by attacks which either do not use the gradient orby attacks which approximate and use the corrected gradient. Defenses that donot obfuscate gradients such as adversarial training exist, but theseapproaches generally make assumptions about the attack such as its magnitude.We propose a classification model that does not obfuscate gradients and isrobust by construction without assuming prior knowledge about the attack. Ourmethod casts classification as an optimization problem where we "invert" aconditional generator trained on unperturbed, natural images to find the classthat generates the closest sample to the query image. We hypothesize that apotential source of brittleness against adversarial attacks is thehigh-to-low-dimensional nature of feed-forward classifiers which allows anadversary to find small perturbations in the input space that lead to largechanges in the output space. On the other hand, a generative model is typicallya low-to-high-dimensional mapping. While the method is related to Defense-GAN,the use of a conditional generative model and inversion in our model instead ofthe feed-forward classifier is a critical difference. Unlike Defense-GAN, whichwas shown to generate obfuscated gradients that are easily circumvented, weshow that our method does not obfuscate gradients. We demonstrate that ourmodel is extremely robust against black-box attacks and has improved robustnessagainst white-box attacks compared to naturally trained, feed-forwardclassifiers.

Adversarially Robust Classification by Conditional Generative Model Inversion