Deep neural nets achieve state-of-the-art performance on the problem of
optical flow estimation. Since optical flow is used in several safety-critical
applications like self-driving cars, it is important to gain insights into the
robustness of those techniques. Recently, it has been shown that adversarial
attacks easily fool deep neural networks to misclassify objects. The robustness
of optical flow networks to adversarial attacks, however, has not been studied
so far. In this paper, we extend adversarial patch attacks to optical flow
networks and show that such attacks can compromise their performance. We show
that corrupting a small patch of less than 1% of the image size can
significantly affect optical flow estimates. Our attacks lead to noisy flow
estimates that extend significantly beyond the region of the attack, in many
cases even completely erasing the motion of objects in the scene. While
networks using an encoder-decoder architecture are very sensitive to these
attacks, we found that networks using a spatial pyramid architecture are less
affected. We analyse the success and failure of attacking both architectures by
visualizing their feature maps and comparing them to classical optical flow
techniques which are robust to these attacks. We also demonstrate that such
attacks are practical by placing a printed pattern into real scenes.

本文研究了深度学习在光流估计中的鲁棒性，并表明对小于 1％的图像区域进行扰动就可以大大影响光流估计，对编码器 - 解码器体系结构的网络影响很大，而对空间金字塔体系结构的网络则较小，并且通过实验演示了这种攻击是实用的。