Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers

• URL: http://arxiv.org/abs/2002.09766v1
• Date: Sat, 22 Feb 2020 20:19:53 GMT
• Title: Improving the Tightness of Convex Relaxation Bounds for Training Certifiably Robust Classifiers
• Authors: Chen Zhu, Renkun Ni, Ping-yeh Chiang, Hengduo Li, Furong Huang, Tom Goldstein
• Abstract summary: Convex relaxations are effective for certifying training and neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical robustness. We propose two experiments that can be used to train neural networks that can be trained in higher certified accuracy than non-regularized baselines.
• Score: 72.56180590447835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convex relaxations are effective for training and certifying neural networks against norm-bounded adversarial attacks, but they leave a large gap between certifiable and empirical robustness. In principle, convex relaxation can provide tight bounds if the solution to the relaxed problem is feasible for the original non-convex problem. We propose two regularizers that can be used to train neural networks that yield tighter convex relaxation bounds for robustness. In all of our experiments, the proposed regularizers result in higher certified accuracy than non-regularized baselines.

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