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• W2964152294 abstract "Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the input data. Although most attacks usually change values of many image's pixels, it has been shown that deep networks are also vulnerable to sparse alterations of the input. However, no computationally efficient method has been proposed to compute sparse perturbations. In this paper, we exploit the low mean curvature of the decision boundary, and propose SparseFool, a geometry inspired sparse attack that controls the sparsity of the perturbations. Extensive evaluations show that our approach computes sparse perturbations very fast, and scales efficiently to high dimensional data. We further analyze the transferability and the visual effects of the perturbations, and show the existence of shared semantic information across the images and the networks. Finally, we show that adversarial training can only slightly improve the robustness against sparse additive perturbations computed with SparseFool." @default.
• W2964152294 created "2019-07-30" @default.
• W2964152294 creator A5000947076 @default.
• W2964152294 creator A5053991440 @default.
• W2964152294 creator A5081421595 @default.
• W2964152294 date "2019-06-01" @default.
• W2964152294 modified "2023-10-05" @default.
• W2964152294 title "SparseFool: A Few Pixels Make a Big Difference" @default.
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• W2964152294 doi "https://doi.org/10.1109/cvpr.2019.00930" @default.
• W2964152294 hasPublicationYear "2019" @default.
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