Adversarial attacks of deep neural networks have been intensively studied on image, audio, natural language, patch, and pixel classification tasks. Nevertheless, as a typical, while important real-world application, the adversarial attacks of online video object tracking that traces an object's moving trajectory instead of its category are rarely explored. In this paper, we identify a new task for the adversarial attack to visual object tracking: online generating imperceptible perturbations that mislead trackers along an incorrect (Untargeted Attack, UA) or specified trajectory (Targeted Attack, TA). To this end, we first propose a spatial-aware basic attack by adapting existing attack methods, i.e., FGSM, BIM, and C\&W, and comprehensively analyze the attacking performance. We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across time/frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency. To address these challenges, we further propose the online incremental attack (OIA) that performs spatial-temporal sparse incremental perturbations online and makes the adversarial attack less perceptible. In addition, as an optimization-based method, OIA quickly converges to very small losses within several iterations by considering historical incremental perturbations, making it much more efficient than the basic attacks. The in-depth evaluation on the state-of-the-art trackers (i.e., SiamRPN with Alex, MobileNetv2, and ResNet-50) for OTB100 and VOT2018 demonstrates the effectiveness and transferability of OIA in misleading existing trackers under both UA and TA with minor perturbations.

通过研究在线视频目标跟踪，本文提出了一种新的对抗攻击任务：在线生成微小扰动来误导跟踪器沿指定或不正确的轨迹跟踪目标。为了应对这一挑战，提出了一种基于稀疏性的增量攻击方法SPARK，该方法在多个基准跟踪器上均能有效地误导跟踪器并且具有较强的可迁移性。

SPARK：面向视觉跟踪的空间感知在线增量攻击