TPatch: A Triggered Physical Adversarial Patch. (arXiv:2401.00148v1 [cs.CR])

Autonomous vehicles increasingly utilize the vision-based perception module
to acquire information about driving environments and detect obstacles. Correct
detection and classification are important to ensure safe driving decisions.
Existing works have demonstrated the feasibility of fooling the perception
models such as object detectors and image classifiers with printed adversarial
patches. However, most of them are indiscriminately offensive to every passing
autonomous vehicle. In this paper, we propose TPatch, a physical adversarial
patch triggered by acoustic signals. Unlike other adversarial patches, …

adversarial autonomous autonomous vehicles classification detect detection driving environments image important information object patch patches physical safe vehicles