TAT: Targeted backdoor attacks against visual object tracking
Ziyi Cheng, Baoyuan Wu, Zhenya Zhang, Jianjun Zhao · Pattern Recognition · 2023
Visual object tracking (VOT) is a fundamental computer vision task that aims to track a target in a sequence of video frames. It has been broadly adopted in safety- and security-critical applications, such as self-driving systems and traffic control systems . However, the VOT models (i.e., the trackers) that rely on third-party training resources face a severe threat of backdoor attacks , which refer to the type of the attacks that poison a portion of training data and mislead the tracker to track a wrong target. A surge of research interest has arisen in backdoor attacks in the domain of image classification , as a measure to expose the potential security risks of the classifiers and inspire new defense techniques. Despite the prosperity of the research in backdoor attacks in image classification , there still lacks investigation in backdoor attacks against VOT, due to their unique challenges: first, the architecture of a VOT model is much more complicated than that of an image classifier; second, VOT targets a sequence of video frames rather than individual images. To bridge the gap, we propose a novel and effective targeted backdoor attack approach TAT specifically against VOT tasks. In particular, TAT includes a basic version TAT-BA that can achieve effective and stealthy backdoor attacks against VOT trackers, and an advanced version TAT-DA that can evade two representative defense techniques. Our large-scale experimental evaluation demonstrates the effectiveness and the stealthiness of TAT . Moreover, we also demonstrate the performances of TAT-BA under real-world settings and the abilities of TAT-DA to counter defense techniques. The code will be available at https://github.com/MisakaZipi/TAT .