A Unified Perspective From Diffuse Deviation to Target Hijacking

In the developing field of visual object tracking, the robustness and resilience of detection modules against adversarial perturbations is critical. Traditional attacks have shown limitations in maintaining long-term deception, which is mainly reflected in that they are often only effective for a short period of time, or only have an impact on a specific single frame of images, rather than continuously and effectively mislead objects in continuous video sequences. Therefore, the tracker tends to quickly recover to the correct tracking state in the face of continuously changing adversarial attacks, showing robustness against occasional detection anomalies. In order to solve these problems, we propose Multi-Strategy Adversarial Attack (MSAA). MSAA imposes specific constraints on the decision-making ability of the model, regulating the priority of modification to candidate bounding boxes, including offset and size. In addition, the strategy to construct a predefined hijacking trajectory includes a direction-aware perturbation and a center matching scheme to hijack the feature-aware module to a predefined target. To the best of our knowledge, this is the first time that a unified perspective is adopted to address the problem from diffuse deviation to target hijacking. Our method not only enhances the persistence and concealment of attacks, but also achieves more precise control in multi-target scenarios, which has not been fully addressed in traditional adversarial attack methods. Experiments show that MSAA greatly outperforms state-of-the-art attack methods on multiple public datasets.