  Recent optical flow methods are almost exclusively judged in terms of
accuracy, while their robustness is often neglected. Although adversarial
attacks offer a useful tool to perform such an analysis, current attacks on
optical flow methods focus on real-world attacking scenarios rather than a
worst case robustness assessment. Hence, in this work, we propose a novel
adversarial attack - the Perturbation-Constrained Flow Attack (PCFA) - that
emphasizes destructivity over applicability as a real-world attack. PCFA is a
global attack that optimizes adversarial perturbations to shift the predicted
flow towards a specified target flow, while keeping the L2 norm of the
perturbation below a chosen bound. Our experiments demonstrate PCFA's
applicability in white- and black-box settings, and show it finds stronger
adversarial samples than previous attacks. Based on these strong samples, we
provide the first joint ranking of optical flow methods considering both
prediction quality and adversarial robustness, which reveals state-of-the-art
methods to be particularly vulnerable. Code is available at
https://github.com/cv-stuttgart/PCFA.
