Stereo matching neural networks often involve a Siamese structure to extract intermediate features from left and right images. The similarity between these intermediate left-right features significantly impacts the accuracy of disparity estimation. In this paper, we introduce a novel adversarial attack approach that generates perturbation noise specifically designed to maximize the discrepancy between left and right image features. Extensive experiments demonstrate the superior capability of our method to induce larger prediction errors in stereo neural networks, e.g. outperforming existing state-of-the-art attack methods by 219% MAE on the KITTI dataset and 85% MAE on the Scene Flow dataset. Additionally, we extend our approach to include a proxy network black-box attack method, eliminating the need for access to stereo neural network. This method leverages an arbitrary network from a different vision task as a proxy to generate adversarial noise, effectively causing the stereo network to produce erroneous predictions. Our findings highlight a notable sensitivity of stereo networks to discrepancies in shallow layer features, offering valuable insights that could guide future research in enhancing the robustness of stereo vision systems.

介绍了一种新颖的对抗攻击方法，通过生成特定设计的扰动噪声，最大化左右图像特征之间的差异，从而在立体神经网络中引起更大的预测误差，并通过代理网络黑盒攻击方法将该方法扩展，消除了对立体神经网络的访问需求，从而使其产生错误预测，这为增强立体视觉系统的稳健性提供了有价值的见解。

立体网络对抗攻击中的左右不一致性