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).
@article{arxiv.2312.06163,
title = {Adversarial Camera Patch: An Effective and Robust Physical-World Attack on Object Detectors},
author = {Kalibinuer Tiliwalidi},
journal= {arXiv preprint arXiv:2312.06163},
year = {2023}
}