Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models

• URL: http://arxiv.org/abs/2003.00378v1
• Date: Sun, 1 Mar 2020 01:45:04 GMT
• Title: Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
• Authors: Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans
• Abstract summary: We study the intrinsic robustness of two common image benchmarks under $ell$ perturbations. We show the existence of a large gap between the robustness limits implied by our theory and the adversarial robustness achieved by current state-of-the-art robust models.
• Score: 87.00072607024026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Starting with Gilmer et al. (2018), several works have demonstrated the inevitability of adversarial examples based on different assumptions about the underlying input probability space. It remains unclear, however, whether these results apply to natural image distributions. In this work, we assume the underlying data distribution is captured by some conditional generative model, and prove intrinsic robustness bounds for a general class of classifiers, which solves an open problem in Fawzi et al. (2018). Building upon the state-of-the-art conditional generative models, we study the intrinsic robustness of two common image benchmarks under $\ell_2$ perturbations, and show the existence of a large gap between the robustness limits implied by our theory and the adversarial robustness achieved by current state-of-the-art robust models. Code for all our experiments is available at https://github.com/xiaozhanguva/Intrinsic-Rob.

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