APE-GAN: Adversarial Perturbation Elimination with GAN

18 Jul 2017 Shiwei Shen Guoqing Jin Ke Gao Yongdong Zhang

Although neural networks could achieve state-of-the-art performance while recongnizing images, they often suffer a tremendous defeat from adversarial examples--inputs generated by utilizing imperceptible but intentional perturbation to clean samples from the datasets. How to defense against adversarial examples is an important problem which is well worth researching... (read more)

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