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Stochastic sparse adversarial attacks
Manon Césaire, Lucas Schott, Hatem Hajri, Sylvain Lamprier, Patrick Gallinari
This paper introduces stochastic sparse adversarial attacks (SSAA), standing as simple, fast and purely noise-based targeted and untargeted attacks of neural network classifiers (NNC). SSAA offer new examples of sparse (or L₀) attacks for which only few methods have been proposed previously. These attacks are devised by exploiting a small-time expansion idea widely used for Markov processes. Experiments on small and large datasets (CIFAR-10 and ImageNet) illustrate several advantages of SSAA in comparison with the-state-of-the-art methods. For instance, in the untargeted case, our method called Voting Folded Gaussian Attack (VFGA) scales efficiently to ImageNet and achieves a significantly lower L₀ score than SparseFool (up to 2/5) while being faster. Moreover, VFGA achieves better L₀ scores on ImageNet than Sparse-RS when both attacks are fully successful on a large number of samples.
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