REAP: A Large-Scale Realistic Adversarial Patch Benchmark. (arXiv:2212.05680v1 [cs.CV])

Machine learning models are known to be susceptible to adversarial
perturbation. One famous attack is the adversarial patch, a sticker with a
particularly crafted pattern that makes the model incorrectly predict the
object it is placed on. This attack presents a critical threat to
cyber-physical systems that rely on cameras such as autonomous cars. Despite
the significance of the problem, conducting research in this setting has been
difficult; evaluating attacks and defenses in the real world is exceptionally
costly while …

adversarial benchmark large patch scale