BATT: Backdoor Attack with Transformation-based Triggers
This addresses security vulnerabilities in AI systems for users deploying DNNs in real-world applications, representing an incremental advance by adapting known transformations to bypass current defenses.
The paper tackles the problem of backdoor attacks in deep neural networks by designing a new attack that uses spatial transformations like rotation and translation as triggers, making it effective in both digital and physical settings and resistant to existing defenses.
Deep neural networks (DNNs) are vulnerable to backdoor attacks. The backdoor adversaries intend to maliciously control the predictions of attacked DNNs by injecting hidden backdoors that can be activated by adversary-specified trigger patterns during the training process. One recent research revealed that most of the existing attacks failed in the real physical world since the trigger contained in the digitized test samples may be different from that of the one used for training. Accordingly, users can adopt spatial transformations as the image pre-processing to deactivate hidden backdoors. In this paper, we explore the previous findings from another side. We exploit classical spatial transformations (i.e. rotation and translation) with the specific parameter as trigger patterns to design a simple yet effective poisoning-based backdoor attack. For example, only images rotated to a particular angle can activate the embedded backdoor of attacked DNNs. Extensive experiments are conducted, verifying the effectiveness of our attack under both digital and physical settings and its resistance to existing backdoor defenses.