The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks

• URL: http://arxiv.org/abs/2305.14188v1
• Date: Tue, 23 May 2023 16:07:58 GMT
• Title: The Best Defense is a Good Offense: Adversarial Augmentation against Adversarial Attacks
• Authors: Iuri Frosio and Jan Kautz
• Abstract summary: $A5$ is a framework to craft a defensive perturbation to guarantee that any attack towards the input in hand will fail. We show effective on-the-fly defensive augmentation with a robustifier network that ignores the ground truth label. We also show how to apply $A5$ to create certifiably robust physical objects.
• Score: 91.56314751983133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many defenses against adversarial attacks (\eg robust classifiers, randomization, or image purification) use countermeasures put to work only after the attack has been crafted. We adopt a different perspective to introduce $A^5$ (Adversarial Augmentation Against Adversarial Attacks), a novel framework including the first certified preemptive defense against adversarial attacks. The main idea is to craft a defensive perturbation to guarantee that any attack (up to a given magnitude) towards the input in hand will fail. To this aim, we leverage existing automatic perturbation analysis tools for neural networks. We study the conditions to apply $A^5$ effectively, analyze the importance of the robustness of the to-be-defended classifier, and inspect the appearance of the robustified images. We show effective on-the-fly defensive augmentation with a robustifier network that ignores the ground truth label, and demonstrate the benefits of robustifier and classifier co-training. In our tests, $A^5$ consistently beats state of the art certified defenses on MNIST, CIFAR10, FashionMNIST and Tinyimagenet. We also show how to apply $A^5$ to create certifiably robust physical objects. Our code at https://github.com/NVlabs/A5 allows experimenting on a wide range of scenarios beyond the man-in-the-middle attack tested here, including the case of physical attacks.

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