publication . Preprint . 2018

Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference

Bao, Ruying; Liang, Sihang; Wang, Qingcan;
Open Access English
  • Published: 20 May 2018
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations intentionally added to the original inputs can fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to extract the semantic features of the input and filter the non-semantic perturbation. FBGAN is pre-trained on the clean dataset in an unsupervised manner, adversarially learning a bidirectional mapping between the high-dimensional data space and the low-dimensional semantic space; also mutual information is applied to disentangle the semantically meaningful features. After the...
free text keywords: Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
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