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Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN by Chen, Feng; Shang, Yunkai; Xu, Bo; Hu, Jincheng is a scholarly article available to read on EtoBox.

What is Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN about?

GAN is a deep-learning based generative approach to generate contents such as images, languages and speeches. Recently, studies have shown that GAN can also be applied to generative adversarial attack examples to fool the machine-learning models. In comparison with the previous non-learning adversarial example attack approaches, the GAN-based adversarial attack example approach can generate the adversarial samples quickly using the GAN architecture every time facing a new sample after training, but meanwhile needs to perturb the attack samples in great quantities, which results in the unpractical application in reality. To address this issue, we propose a new approach, named Few-Feature-Attack-GAN (FFA-GAN). FFA-GAN has a significant time-consuming advantage than the non-learning adversarial samples approaches and a better non-zero-features performance than the GANbased adversarial sample approaches. FFA-GAN can automatically generate the attack samples in the black-box attack through the GAN architecture instead of the evolutional algorithms or the other non-learning approaches. Besides, we introduce the mask mechanism into the generator network of the GAN architecture to optimize

Author

Chen, Feng; Shang, Yunkai; Xu, Bo; Hu, Jincheng

Published

2019

Language

EN