Self-driving cars are sensitive to color patches attacks, patches artfully made to interfere with optical estimation

Attacking Optical Flow. Anurag Ranjan, Joel Janai, Andreas Geiger, Michael J. Black. arXiv, Oct 22 2019. https://arxiv.org/pdf/1910.10053.pdf

Abstract: Deep neural nets achieve state-of-the-art performanceon 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 thatadversarial 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 estimatesthat extend significantly beyond the region of the attack, inmany 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.