Improving Transferability of Adversarial Examples with Input Diversity Improving Transferability of Adversarial Examples with Input Diversity
Paper summary Xie et al. propose to improve the transferability of adversarial examples by computing them based on transformed input images. In particular, they adapt I-FGSM such that, in each iteration, the update is computed on a transformed version of the current image with probability $p$. When, at the same time attacking an ensemble of networks, this is shown to improve transferability. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
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Improving Transferability of Adversarial Examples with Input Diversity
Cihang Xie and Zhishuai Zhang and Yuyin Zhou and Song Bai and Jianyu Wang and Zhou Ren and Alan Yuille
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.CV, cs.LG, stat.ML

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Summary by David Stutz 2 months ago
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