论文标题
对抗雪的进攻运动估算
Attacking Motion Estimation with Adversarial Snow
论文作者
论文摘要
当前的对抗性攻击以进行运动估计（光流）优化了每像素扰动的小扰动，这些扰动不太可能出现在现实世界中。相比之下，我们利用了现实世界中的天气现象，以对抗优化的雪进行新的攻击。我们攻击的核心是一个可区分的渲染器，它始终将具有现实运动的影片雪花整合到3D场景中。通过优化，我们获得了对抗性雪，可显着影响光流，同时与普通雪没有区别。令人惊讶的是，我们新颖的攻击的影响最大，对以前显示出对小L_P扰动的鲁棒性的方法最大。
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.
