Multi-sensor fusion (MSF) is widely used in autonomous vehicles (AVs) for perception, particularly for 3D object detection with camera and LiDAR sensors. The purpose of fusion is to capitalize on the advantages of each modality while minimizing its weaknesses. Advanced deep neural network (DNN)-based fusion techniques have demonstrated the exceptional and industry-leading performance. Due to the redundant information in multiple modalities, MSF is also recognized as a general defence strategy against adversarial attacks. In this paper, we attack fusion models from the camera modality that is considered to be of lesser importance in fusion but is more affordable for attackers. We argue that the weakest link of fusion models depends on their most vulnerable modality, and propose an attack framework that targets advanced camera-LiDAR fusion-based 3D object detection models through camera-only adversarial attacks. Our approach employs a two-stage optimization-based strategy that first thoroughly evaluates vulnerable image areas under adversarial attacks, and then applies dedicated attack strategies for different fusion models to generate deployable patches. The evaluations with six advanced camera-LiDAR fusion models and one camera-only model indicate that our attacks successfully compromise all of them. Our approach can either decrease the mean average precision (mAP) of detection performance from 0.824 to 0.353, or degrade the detection score of a target object from 0.728 to 0.156, demonstrating the efficacy of our proposed attack framework. Code is available.

翻译：多传感器融合（MSF）广泛应用于自动驾驶汽车（AV）的感知任务，特别是基于摄像头和激光雷达传感器的3D目标检测。融合的目的在于发挥每种模态的优势，同时最小化其弱点。先进的基于深度神经网络（DNN）的融合技术已展现出卓越且行业领先的性能。由于多模态信息存在冗余，MSF也被视为抵御对抗攻击的通用防御策略。本文从摄像头模态（该模态在融合中通常被认为重要性较低，但攻击成本更低）对融合模型发起攻击。我们认为融合模型的最薄弱环节取决于其最易受攻击的模态，并提出一种攻击框架，通过仅针对摄像头的对抗攻击，目标直指先进的摄像头-激光雷达融合3D目标检测模型。该方法采用两阶段优化策略：首先全面评估受对抗攻击影响的脆弱图像区域，然后针对不同融合模型采用专用攻击策略生成可部署的对抗补丁。对六种先进摄像头-激光雷达融合模型及一种纯摄像头模型的评估表明，我们的攻击成功攻破了所有模型。该方法可将检测性能的平均精度均值（mAP）从0.824降至0.353，或将目标物体的检测得分从0.728降至0.156，充分证明了所提攻击框架的有效性。代码已公开。