Generating Adversarial Examples with Better Transferability via Masking Unimportant Parameters of Surrogate Model. (arXiv:2304.06908v1 [cs.LG])

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial
examples. Moreover, the transferability of the adversarial examples has
received broad attention in recent years, which means that adversarial examples
crafted by a surrogate model can also attack unknown models. This phenomenon
gave birth to the transfer-based adversarial attacks, which aim to improve the
transferability of the generated adversarial examples. In this paper, we
propose to improve the transferability of adversarial examples in the
transfer-based attack via masking …

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