A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking
Zhenbang Li, Yaya Shi, Jin Gao, Shaoru Wang, Bing Li, Pengpeng Liang,, Weiming Hu

TL;DR
This paper introduces a universal, video-agnostic adversarial attack method for Siamese visual trackers that requires no additional inference, effectively fooling trackers across different videos and architectures with minimal computational cost.
Contribution
The authors propose a novel universal targeted attack approach that is both data-agnostic and network-agnostic, eliminating the need for per-video optimization.
Findings
Effective fooling of Siamese trackers with universal perturbations
Perturbations generalize across different videos and models
Attack operates with minimal computational overhead
Abstract
Siamese trackers are shown to be vulnerable to adversarial attacks recently. However, the existing attack methods craft the perturbations for each video independently, which comes at a non-negligible computational cost. In this paper, we show the existence of universal perturbations that can enable the targeted attack, e.g., forcing a tracker to follow the ground-truth trajectory with specified offsets, to be video-agnostic and free from inference in a network. Specifically, we attack a tracker by adding a universal imperceptible perturbation to the template image and adding a fake target, i.e., a small universal adversarial patch, into the search images adhering to the predefined trajectory, so that the tracker outputs the location and size of the fake target instead of the real target. Our approach allows perturbing a novel video to come at no additional cost except the mere addition…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Video Surveillance and Tracking Methods · Anomaly Detection Techniques and Applications
