Abstract
Visual object tracking is an important task that requires the tracker to find the objects quickly and accurately. The existing state-of-the-art object trackers, i.e., Siamese based trackers, use DNNs to attain high accuracy. However, the robustness of visual tracking models is seldom explored. In this paper, we analyze the weakness of object trackers based on the Siamese network and then extend adversarial examples to visual object tracking. We present an end-to-end network FAN (Fast Attack Network) that uses a novel drift loss combined with the embedded feature loss to attack the Siamese network based trackers. Under a single GPU, FAN is efficient in the training speed and has a strong attack performance. The FAN can generate an adversarial example at 10ms, achieve effective targeted attack (at least 40% drop rate on OTB) and untargeted attack (at least 70% drop rate on OTB).
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Acknowledgement
Supported by the National Key R&D Program of China (Grant No. 2018AAA010 0600), National Natural Science Foundation of China (No. U1636214, 61861166002, No. 61806109), Beijing Natural Science Foundation (No. L182057), Zhejiang Lab (NO. 2019NB0AB01), Peng Cheng Laboratory Project of Guangdong Province PCL2018KP004.
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Liang, S., Wei, X., Yao, S., Cao, X. (2020). Efficient Adversarial Attacks for Visual Object Tracking. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12371. Springer, Cham. https://doi.org/10.1007/978-3-030-58574-7_3
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