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Few-Shot Backdoor Attacks on Visual Object Tracking. (arXiv:2201.13178v2 [cs.CV] UPDATED)
Web: http://arxiv.org/abs/2201.13178
May 5, 2022, 1:10 a.m. | Yiming Li, Haoxiang Zhong, Xingjun Ma, Yong Jiang, Shu-Tao Xia
cs.CV updates on arXiv.org arxiv.org
Visual object tracking (VOT) has been widely adopted in mission-critical
applications, such as autonomous driving and intelligent surveillance systems.
In current practice, third-party resources such as datasets, backbone networks,
and training platforms are frequently used to train high-performance VOT
models. Whilst these resources bring certain convenience, they also introduce
new security threats into VOT models. In this paper, we reveal such a threat
where an adversary can easily implant hidden backdoors into VOT models by
tempering with the training process. …
More from arxiv.org / cs.CV updates on arXiv.org
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