WANET -IMPERCEPTIBLE WARPING-BASED BACK-DOOR ATTACK

Anh Nguyen, Anh Tran

arXiv · PDF · Open in the Atlas

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Abstract

With the thriving of deep learning and the widespread practice of using pretrained 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. Our code is publicly available at https://github.com/VinAIResearch/ Warping-based_Backdoor_Attack-release.

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