  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.
