ImAdv: Transferable Implicit Adversarial Attack for 3D Object Detectors in Autonomous Driving

Zijian Zhu, Xiao Yang, Hang Su, Shu Zhao, Shibao Zheng

Abstract: 3D adversarial attacks have garnered significant attention in the realm of autonomous driving security due to their high feasibility and multi-view effectiveness. However, existing 3D attacks have limited transferability, primarily due to their overfitting to surrogate models. To address this limitation, we introduce a novel 3D adversarial attack method based on implicit texture modeling, termed ImAdv, against 3D object detection models. Specifically, ImAdv utilizes a positional encoder and a MLP to map the 3D coordinates of an object’s surface to the RGB color space, thereby reformulating the object’s texture within an implicit framework. This method significantly reduces the parameter number for color modeling, thus mitigating overfitting and improving transferability. Furthermore, we propose two innovative techniques to enhance the transferability, Random Texture Reset (RandReset) and Texture Model Averaging. RandReset randomly restores portions of the adversarial texture, increasing the training set diversity and mitigating overfitting. Texture Model Averaging employs self-ensembling of multiple texture checkpoints during the training phase to reduce overfitting in the final texture model. Comprehensive experiments demonstrate the superiority of our methods, which outperform previous methods by 17.18% in average black-box attack success rate. Additionally, our method shows strong transferability and practicality in zero-shot cross-task attacks and physical attacks.

External IDs:dblp:journals/tbd/ZhuYSZZ25