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In recent years, the trackers based on Siamese networks have emerged ashighly effective and efficient for visual object tracking (VOT). While thesemethods were shown to be vulnerable to adversarial attacks, as most deepnetworks for visual recognition tasks, the existing attacks for VOT trackersall require perturbing the search region of every input frame to be effective,which comes at a non-negligible cost, considering that VOT is a real-time task.In this paper, we propose a framework to generate a single temporallytransferable adversarial perturbation from the object template image only. Thisperturbation can then be added to every search image, which comes at virtuallyno cost, and still, successfully fool the tracker. Our experiments evidencethat our approach outperforms the state-of-the-art attacks on the standard VOTbenchmarks in the untargeted scenario. Furthermore, we show that our formalismnaturally extends to targeted attacks that force the tracker to follow anygiven trajectory by precomputing diverse directional perturbations.

Temporally-Transferable Perturbations: Efficient, One-Shot Adversarial Attacks for Online Visual Object Trackers