论文标题
部分可观测时空混沌系统的无模型预测
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow
论文作者
论文摘要
最近的光流方法几乎完全根据精度来判断，而它们的鲁棒性通常被忽略。尽管对抗性攻击提供了执行此类分析的有用工具，但是当前对光流方法的攻击集中在现实世界中的攻击场景上，而不是最坏的情况下的鲁棒性评估。因此，在这项工作中，我们提出了一种新颖的对抗性攻击 - 受扰动约束的流动攻击（PCFA），该攻击强调了对适用性的破坏性作为现实世界攻击。 PCFA是一种全局攻击，可优化对抗性扰动，以将预测的流向指定的目标流动，同时将扰动的L2标准保持在所选界限之下。我们的实验证明了PCFA在白色和黑色盒子设置中的适用性，并证明它发现比以前的攻击更强。基于这些强大的样本，我们考虑了考虑预测质量和对抗性鲁棒性的光流方法的第一个关节排名，这揭示了最新的方法特别脆弱。代码可在https://github.com/cv-stuttgart/pcfa上找到。
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.
