Adversarial patches undermine the reliability of optical flow predictions when placed in arbitrary scene locations. Therefore, they pose a realistic threat to real-world motion detection and its downstream applications. Potential remedies are defense strategies that detect and remove adversarial patches, but their influence on the underlying motion prediction has not been investigated. In this paper, we thoroughly examine the currently available detect-and-remove defenses ILP and LGS for a wide selection of state-of-the-art optical flow methods, and illuminate their side effects on the quality and robustness of the final flow predictions. In particular, we implement defense-aware attacks to investigate whether current defenses are able to withstand attacks that take the defense mechanism into account. Our experiments yield two surprising results: Detect-and-remove defenses do not only lower the optical flow quality on benign scenes, in doing so, they also harm the robustness under patch attacks for all tested optical flow methods except FlowNetC. As currently employed detect-and-remove defenses fail to deliver the promised adversarial robustness for optical flow, they evoke a false sense of security. The code is available at https://github.com/cv-stuttgart/DetectionDefenses.

翻译：对抗性补丁在任意场景位置放置时，会破坏光流预测的可靠性。因此，它们对实际运动检测及其下游应用构成了现实威胁。潜在的补救措施是检测并移除对抗性补丁的防御策略，但这些策略对底层运动预测的影响尚未被研究。本文全面考察了当前可用的检测-移除防御方法ILP和LGS，针对多种先进光流方法，并揭示了它们对最终流预测质量和鲁棒性的副作用。具体而言，我们实现了防御感知攻击，以探究当前防御能否经受住考虑防御机制的针对性攻击。实验结果揭示了两个令人惊讶的现象：检测-移除防御不仅会降低良性场景中的光流质量，还损害了除FlowNetC外所有测试光流方法在补丁攻击下的鲁棒性。由于当前采用的检测-移除防御未能实现其承诺的光流对抗鲁棒性，它们引发了虚假的安全感。代码见https://github.com/cv-stuttgart/DetectionDefenses。