Feb. 12, 2024, 5:46 a.m. | Amira Guesmi Ioan Marius Bilasco Muhammad Shafique Ihsen Alouani

cs.CV updates on arXiv.org arxiv.org

Physical adversarial attacks pose a significant practical threat as it deceives deep learning systems operating in the real world by producing prominent and maliciously designed physical perturbations. Emphasizing the evaluation of naturalness is crucial in such attacks, as humans can readily detect and eliminate unnatural manipulations. To overcome this limitation, recent work has proposed leveraging generative adversarial networks (GANs) to generate naturalistic patches, which may not catch human's attention. However, these approaches suffer from a limited latent space which leads …

adversarial adversarial attacks art attacks cs.cr cs.cv deep learning detection evaluation humans learning systems practical systems threat work world

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