AE-GAN: adversarial eliminating with GAN AE-GAN: adversarial eliminating with GAN
Paper summary Shen et al. introduce APE-GAN, a generative adversarial network (GAN) trained to remove adversarial noise from adversarial examples. In specific, as illustrated in Figure 1, a GAN is traiend to specifically distinguish clean/real images from adversarial images. The generator is conditioned on th einput image and can be seen as auto encoder. Then, during testing, the generator is applied to remove the adversarial noise. https://i.imgur.com/mgAbzCT.png Figure 1: The proposed adversarial perturbation eliminating GAN (APE-GAN), see the paper for details. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
arxiv.org
scholar.google.com
AE-GAN: adversarial eliminating with GAN
Shen, Shiwei and Jin, Guoqing and Gao, Ke and Zhang, Yongdong
arXiv e-Print archive - 2017 via Local Bibsonomy
Keywords: dblp


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Summary by David Stutz 1 month ago
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