  Current adversarial attacks for motion estimation (optical flow) optimize
small per-pixel perturbations, which are unlikely to appear in the real world.
In contrast, we exploit a real-world weather phenomenon for a novel attack with
adversarially optimized snow. At the core of our attack is a differentiable
renderer that consistently integrates photorealistic snowflakes with realistic
motion into the 3D scene. Through optimization we obtain adversarial snow that
significantly impacts the optical flow while being indistinguishable from
ordinary snow. Surprisingly, the impact of our novel attack is largest on
methods that previously showed a high robustness to small L_p perturbations.
