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.

为解决深度神经网络对场景流网络的鲁棒性问题，本研究通过引入针对场景流网络的白盒对抗攻击，发现对一维点云或颜色通道进行攻击对平均端点误差具有显著影响。实验结果显示生成的对抗性样本使 KITTI 和 FlyingThings3D 数据集上的平均端点误差相对劣化达到 33.7。此外，对场景流网络和其 2D 光流网络变体的攻击成功和失败的分析表明光流网络更加容易受到攻击。