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Deep neural networks have been demonstrated to be vulnerable to adversarialattacks, where small perturbations intentionally added to the original inputscan fool the classifier. In this paper, we propose a defense method, FeaturizedBidirectional Generative Adversarial Networks (FBGAN), to extract the semanticfeatures of the input and filter the non-semantic perturbation. FBGAN ispre-trained on the clean dataset in an unsupervised manner, adversariallylearning a bidirectional mapping between the high-dimensional data space andthe low-dimensional semantic space; also mutual information is applied todisentangle the semantically meaningful features. After the bidirectionalmapping, the adversarial data can be reconstructed to denoised data, whichcould be fed into any pre-trained classifier. We empirically show the qualityof reconstruction images and the effectiveness of defense.

Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference