MonoAttack: A Strong Attack Framework with Depth-Migration and Attribute-Tampering for Monocular 3D Object Detection

Xiayue Zhang, Huashuo Lei, Daizong Liu, Xiaoye Qu, Xiang Fang, Runwei Guan, Keyan Jin

Abstract: Although many efforts have been made into attacks on deep neural networks (DNNs) in recent years, no research explores the vulnerability of monocular 3D object detection (M3D) models. This M3D task is fundamental but essential in safety-critical 3D applications, potentially bringing hazards to autonomous driving. In this paper, we thoroughly investigate the sensitivity of current M3D models to adversarial noise and propose a novel M3D adversarial attack method called MonoAttack. The key insight of our method is exploring both depth-migration and attribute-tampering for generating M3D adversarial samples. Specifically, in addition to the general misleading of the detection model, we deceive the M3D model by changing the potential object depth into its opposite position. We also guide the M3D model to mis-recognize the class attribute of its detected object for generating low-confidence bounding boxes. Moreover, we further disentangle the depth knowledge from the geometric and semantic perspectives to auxiliary correlate the detection and attribute information for jointly generating the latent perturbation. In this manner, our attack framework is strong and can effectively attack M3D models with trivial perturbations. Experimental results on the KITTI dataset demonstrate that our attack achieves high adversarial ability against current monocular 3D detection models.

External IDs:dblp:conf/ijcnn/ZhangLLQFGJ25