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Approximate better, Attack stronger: Adversarial Example Generation via Asymptotically Gaussian Mixture Distribution. (arXiv:2209.11964v1 [cs.LG])
Sept. 27, 2022, 1:12 a.m. | Zhengwei Fang, Rui Wang, Tao Huang, Liping Jing
cs.CV updates on arXiv.org arxiv.org
Strong adversarial examples are the keys to evaluating and enhancing the
robustness of deep neural networks. The popular adversarial attack algorithms
maximize the non-concave loss function using the gradient ascent. However, the
performance of each attack is usually sensitive to, for instance, minor image
transformations due to insufficient information (only one input example, few
white-box source models and unknown defense strategies). Hence, the crafted
adversarial examples are prone to overfit the source model, which limits their
transferability to unidentified architectures. …
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