Using Honeypots to Catch Adversarial Attacks on Neural Networks. (arXiv:1904.08554v4 [cs.LG] UPDATED)

4 years ago

Using Honeypots to Catch Adversarial Attacks on Neural Networks. (arXiv:1904.08554v4 [cs.LG] UPDATED)

Shawn Shan, Emily Wenger, Bolun Wang, Bo Li, Haitao Zheng, Ben Y. Zhao

Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to compute adversarial examples that exploit them. In our work, we explore a new "honeypot" approach to protect DNN models. We intentionally inject trapdoors, honeypot weaknesses in the classification manifold that attract attackers searching for adversarial examples. Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space. Our defense then identifies attacks by comparing neuron activation signatures of inputs to those of trapdoors. In this paper, we introduce trapdoors and describe an implementation of a trapdoor-enabled defense. First, we analytically prove that trapdoors shape the computation of adversarial attacks so that attack inputs will have feature representations very similar to those of trapdoors. Second, we experimentally show that trapdoor-protected models can detect, with high accuracy, adversarial examples generated by state-of-the-art attacks (Projected Gradient Descent, optimization-based CW, Elastic Net, BPDA), with negligible impact on normal classification. These results generalize across classification domains, including image, facial, and traffic-sign recognition. We also validate trapdoors' robustness against strong adaptive attacks (countermeasures), including those who can identify and unlearn trapdoors.

Publisher URL: http://arxiv.org/abs/1904.08554

DOI: arXiv:1904.08554v4