Robustness of deep neural network classifiers has been attracting increased attention. As for the robust classification problem, a generative classifier typically models the distribution of inputs and labels, and thus can better handle off-manifold examples at the cost of a concise structure. On the contrary, a discriminative classifier only models the conditional distribution of labels given inputs, but benefits from effective optimization owing to its succinct structure. This work aims for a solution of generative classifiers that can profit from the merits of both. To this end, we propose an Anti-Perturbation Inference (API) method, which searches for anti-perturbations to maximize the lower bound of the joint log-likelihood of inputs and classes. By leveraging the lower bound to approximate Bayes’ rule, we construct a generative classifier Anti-Perturbation Inference Net (API-Net) upon a single discriminator. It takes advantage of the generative properties to tackle off-manifold examples while maintaining a succinct structure for effective optimization. Experiments show that API successfully neutralizes adversarial perturbations, and API-Net consistently outperforms state-of-the-art defenses on prevailing benchmarks, including CIFAR-10, MNIST, and SVHN.(Our code is available at github.com/dongxinshuai/API-Net.).
CITATION STYLE
Dong, X., Liu, H., Ji, R., Cao, L., Ye, Q., Liu, J., & Tian, Q. (2020). API-Net: Robust Generative Classifier via a Single Discriminator. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12358 LNCS, pp. 379–394). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-58601-0_23
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