  While deep neural networks (DNNs) achieve impressive performance on
environment perception tasks, their sensitivity to adversarial perturbations
limits their use in practical applications. In this paper, we (i) propose a
novel adversarial perturbation detection scheme based on multi-task perception
of complex vision tasks (i.e., depth estimation and semantic segmentation).
Specifically, adversarial perturbations are detected by inconsistencies between
extracted edges of the input image, the depth output, and the segmentation
output. To further improve this technique, we (ii) develop a novel edge
consistency loss between all three modalities, thereby improving their initial
consistency which in turn supports our detection scheme. We verify our
detection scheme's effectiveness by employing various known attacks and image
noises. In addition, we (iii) develop a multi-task adversarial attack, aiming
at fooling both tasks as well as our detection scheme. Experimental evaluation
on the Cityscapes and KITTI datasets shows that under an assumption of a 5%
false positive rate up to 100% of images are correctly detected as
adversarially perturbed, depending on the strength of the perturbation. Code is
available at https://github.com/ifnspaml/AdvAttackDet. A short video at
https://youtu.be/KKa6gOyWmH4 provides qualitative results.

Computer Vision and Pattern Recognition