# DAVIDSTUTZ

22thSEPTEMBER2019

Cihang Xie, Zhishuai Zhang, Jianyu Wang, Yuyin Zhou, Zhou Ren, Alan L. Yuille. Improving Transferability of Adversarial Examples with Input Diversity. CoRR abs/1803.06978 (2018).

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 on ShortScience.org.

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