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
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/).
