Adversarial Robustness by Design through Analog Computing and Synthetic Gradients

• URL: http://arxiv.org/abs/2101.02115v1
• Date: Wed, 6 Jan 2021 16:15:29 GMT
• Title: Adversarial Robustness by Design through Analog Computing and Synthetic Gradients
• Authors: Alessandro Cappelli, Ruben Ohana, Julien Launay, Laurent Meunier, Iacopo Poli, Florent Krzakala
• Abstract summary: We propose a new defense mechanism against adversarial attacks inspired by an optical co-processor. In the white-box setting, our defense works by obfuscating the parameters of the random projection. We find the combination of a random projection and binarization in the optical system also improves robustness against various types of black-box attacks.
• Score: 80.60080084042666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new defense mechanism against adversarial attacks inspired by an optical co-processor, providing robustness without compromising natural accuracy in both white-box and black-box settings. This hardware co-processor performs a nonlinear fixed random transformation, where the parameters are unknown and impossible to retrieve with sufficient precision for large enough dimensions. In the white-box setting, our defense works by obfuscating the parameters of the random projection. Unlike other defenses relying on obfuscated gradients, we find we are unable to build a reliable backward differentiable approximation for obfuscated parameters. Moreover, while our model reaches a good natural accuracy with a hybrid backpropagation - synthetic gradient method, the same approach is suboptimal if employed to generate adversarial examples. We find the combination of a random projection and binarization in the optical system also improves robustness against various types of black-box attacks. Finally, our hybrid training method builds robust features against transfer attacks. We demonstrate our approach on a VGG-like architecture, placing the defense on top of the convolutional features, on CIFAR-10 and CIFAR-100. Code is available at https://github.com/lightonai/adversarial-robustness-by-design.

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