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Generating Semantic Adversarial Examples via Feature Manipulation · Around

2020

Generating Semantic Adversarial Examples via Feature Manipulation

Wang, Shuo, Nepal, Surya, Rudolph, Carsten et al.

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The vulnerability of deep neural networks to adversarial attacks has been widely demonstrated (e.g., adversarial example attacks).

Traditional attacks perform unstructured pixel-wise perturbation to fool the classifier.
An alternative approach is to have perturbations in the latent space.
However, such perturbations are hard to control due to the lack of interpretability and disentanglement.

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Similar

N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in Security and Privacy (EuroS&P), 2016 IEEE European Symposium on . IEEE, 2016, pp. 372–387
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Y. Liu, X. Chen, C. Liu, and D. Song, “Delving into transferable adversarial examples and black-box attacks,” arXiv preprint arXiv:1611.02770 , 2016
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X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “Infogan: Interpretable representation learning by information maximizing generative adversarial nets,” in Advances in neural information processing systems , 2016, pp. 2172–2180
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Then

D. Meng and H. Chen, “Magnet: a two-pronged defense against adversarial examples,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2017, pp. 135–147
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