A comparative study of phantom sponge for monocular 3D object detection on edge devices

Abstract

In this paper, we expand the Phantom Sponge attack to monocular 3D object detection to increase false positives and detection times, thereby impairing the performance of edge devices. We introduce alpha-blending with a Universal Adversarial Patch (UAP) to modulate attack strength. Extensive experiments on KITTI, Rope3D, and NuScenes datasets across various hardware GPUs assess the UAP's impact. We also analyze power consumption, temperature, and other metrics on NVIDIA Jetson devices under UAP attacks. Our findings reveal the vulnerability of NMS-dependent models and edge devices to UAP attacks, the generalization of UAP, and the need for robust defenses in real-time applications.