Dynamic Backdoor Attacks Against Deep Neural Networks
Current Deep Neural Network (DNN) backdooring attacks rely on adding static triggers (with fixed patterns and locations) on model inputs that are prone to detection. In this paper, we propose the first class of dynamic backdooring techniques: Random Backdoor, Backdoor Generating Network (BaN), and conditional Backdoor Generating Network (c-BaN). Triggers generated by our techniques have random patterns and locations. In particular, BaN and c-BaN based on a novel generative network are the first two schemes that algorithmically generate triggers. Moreover, c-BaN is the first conditional backdooring technique that given a target label, it can generate a target-specific trigger. Both BaN and c-BaN are essentially a general framework which renders the adversary the flexibility for further customizing backdoor attacks. We extensively evaluate our techniques on three benchmark datasets and show that our techniques achieve almost perfect attack performance on backdoored data with a negligible utility loss. More importantly, our techniques can bypass state-of-the-art defense mechanisms.
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