Rethinking the Trigger of Backdoor Attack
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In this work, we study the problem of backdoor attacks, which add a specific trigger (i.e., a local patch) onto some training images to enforce that the testing images with the same trigger are incorrectly predicted while the natural testing examples are correctly predicted by the trained model. Many existing works adopted the setting that the triggers across the training and testing images follow the same appearance and are located at the same area. However, we observe that if the appearance or location of the trigger is slightly changed, then the attack performance may degrade sharply. According to this observation, we propose to spatially transform (e.g., flipping and scaling) the testing image, such that the appearance and location of the trigger (if exists) will be changed. This simple strategy is experimentally verified to be effective to defend many state-of-the-art backdoor attack methods. Furthermore, to enhance the robustness of the backdoor attacks, we propose to conduct the random spatial transformation on the training images with the trigger before feeding into the training process. Extensive experiments verify that the proposed backdoor attack is robust to spatial transformations.
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