Distracting Downpour: Adversarial Weather Attacks for Motion Estimation

Jenny Schmalfuss, Lukas Mehl, Andrés Bruhn; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 10106-10116


Current adversarial attacks on motion estimation, or optical flow, optimize small per-pixel perturbations, which are unlikely to appear in the real world. In contrast, adverse weather conditions constitute a much more realistic threat scenario. Hence, in this work, we present a novel attack on motion estimation that exploits adversarially optimized particles to mimic weather effects like snowflakes, rain streaks or fog clouds. At the core of our attack framework is a differentiable particle rendering system that integrates particles (i) consistently over multiple time steps (ii) into the 3D space (iii) with a photo-realistic appearance. Through optimization, we obtain adversarial weather that significantly impacts the motion estimation. Surprisingly, methods that previously showed good robustness towards small per-pixel perturbations are particularly vulnerable to adversarial weather. At the same time, augmenting the training with non-optimized weather increases a method's robustness towards weather effects and improves generalizability at almost no additional cost. Our code is available at https://github.com/cv-stuttgart/DistractingDownpour.

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@InProceedings{Schmalfuss_2023_ICCV, author = {Schmalfuss, Jenny and Mehl, Lukas and Bruhn, Andr\'es}, title = {Distracting Downpour: Adversarial Weather Attacks for Motion Estimation}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {10106-10116} }