Efficient and Transferable Adversarial Examples from Bayesian Neural NetworksDownload PDF

Published: 20 May 2022, Last Modified: 22 Oct 2023UAI 2022 PosterReaders: Everyone
Keywords: Adversarial Examples, Adversarial Machine Learning, Machine Learning Security, Bayesian Deep Learning, Ensemble Methods, Machine Learning, Deep Learning
TL;DR: cSGLD, a Bayesian Deep Learning method, is an efficient surrogate to craft adversarial examples transferable to deterministic DNN targets
Abstract: An established way to improve the transferability of black-box evasion attacks is to craft the adversarial examples on an ensemble-based surrogate to increase diversity. We argue that transferability is fundamentally related to uncertainty. Based on a state-of-the-art Bayesian Deep Learning technique, we propose a new method to efficiently build a surrogate by sampling approximately from the posterior distribution of neural network weights, which represents the belief about the value of each parameter. Our extensive experiments on ImageNet, CIFAR-10 and MNIST show that our approach improves the success rates of four state-of-the-art attacks significantly (up to 83.2 percentage points), in both intra-architecture and inter-architecture transferability. On ImageNet, our approach can reach 94% of success rate while reducing training computations from 11.6 to 2.4 exaflops, compared to an ensemble of independently trained DNNs. Our vanilla surrogate achieves 87.5% of the time higher transferability than three test-time techniques designed for this purpose. Our work demonstrates that the way to train a surrogate has been overlooked, although it is an important element of transfer-based attacks. We are, therefore, the first to review the effectiveness of several training methods in increasing transferability. We provide new directions to better understand the transferability phenomenon and offer a simple but strong baseline for future work.
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