WaNet -- Imperceptible Warping-based Backdoor Attack

Anh Nguyen; Anh Tran

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arXiv · 2102.10369

WaNet -- Imperceptible Warping-based Backdoor Attack

Anh Nguyen·Anh Tran

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Abstract

With the thriving of deep learning and the widespread practice of using pre-trained networks, backdoor attacks have become an increasing security threat drawing many research interests in recent years. A third-party model can be poisoned in training to work well in normal conditions but behave maliciously when a trigger pattern appears. However, the existing backdoor attacks are all built on noise perturbation triggers, making them noticeable to humans. In this paper, we instead propose using warping-based triggers. The proposed backdoor outperforms the previous methods in a human inspection test by a wide margin, proving its stealthiness. To make such models undetectable by machine defenders, we propose a novel training mode, called the ``noise mode. The trained networks successfully attack and bypass the state-of-the-art defense methods on standard classification datasets, including MNIST, CIFAR-10, GTSRB, and CelebA. Behavior analyses show that our backdoors are transparent to network inspection, further proving this novel attack mechanism's efficiency.

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Anh Nguyen, Anh Tran. 2021-02-20. WaNet -- Imperceptible Warping-based Backdoor Attack. https://arxiv.org/abs/2102.10369

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