  Nowadays, the susceptibility of deep neural networks (DNNs) has garnered
significant attention. Researchers are exploring patch-based physical attacks,
yet traditional approaches, while effective, often result in conspicuous
patches covering target objects. This leads to easy detection by human
observers. Recently, novel camera-based physical attacks have emerged,
leveraging camera patches to execute stealthy attacks. These methods circumvent
target object modifications by introducing perturbations directly to the camera
lens, achieving a notable breakthrough in stealthiness. However, prevailing
camera-based strategies necessitate the deployment of multiple patches on the
camera lens, which introduces complexity. To address this issue, we propose an
Adversarial Camera Patch (ADCP).
