Deep neural networks have made significant advancements in accurately estimating scene flow using point clouds, which is vital for many applications like video analysis, action recognition, and navigation. Robustness of these techniques, however, remains a concern, particularly in the face of adversarial attacks that have been proven to deceive state-of-the-art deep neural networks in many domains. Surprisingly, the robustness of scene flow networks against such attacks has not been thoroughly investigated. To address this problem, the proposed approach aims to bridge this gap by introducing adversarial white-box attacks specifically tailored for scene flow networks. Experimental results show that the generated adversarial examples obtain up to 33.7 relative degradation in average end-point error on the KITTI and FlyingThings3D datasets. The study also reveals the significant impact that attacks targeting point clouds in only one dimension or color channel have on average end-point error. Analyzing the success and failure of these attacks on the scene flow networks and their 2D optical flow network variants show a higher vulnerability for the optical flow networks.

翻译：深度神经网络在利用点云精确估计场景光流方面取得了显著进展，这对视频分析、动作识别和导航等众多应用至关重要。然而，这些技术的鲁棒性仍是一个问题，尤其是在面对已被证明能在多个领域欺骗最先进深度神经网络的对抗性攻击时。令人惊讶的是，场景光流网络针对此类攻击的鲁棒性尚未得到充分研究。为解决这一问题，所提出的方法旨在通过引入专门针对场景光流网络的对抗性白盒攻击来填补这一空白。实验结果表明，生成的对抗样本在KITTI和FlyingThings3D数据集中，平均端点误差相对降低高达33.7%。研究还揭示了仅针对点云单一维度或颜色通道的攻击对平均端点误差的显著影响。对场景光流网络及其二维光流网络变体上攻击成功与失败的分析显示，光流网络具有更高的脆弱性。