{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/physical-adversarial-attacks-against-end-to","title":"Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems","arxiv_id":"1902.08391","date":"2019-02-22","proceeding":null,"authors":["Meysam Sadeghi","Erik G. Larsson"],"abstract":"We show that end-to-end learning of communication systems through deep neural\nnetwork (DNN) autoencoders can be extremely vulnerable to physical adversarial\nattacks. Specifically, we elaborate how an attacker can craft effective\nphysical black-box adversarial attacks. Due to the openness (broadcast nature)\nof the wireless channel, an adversary transmitter can increase the\nblock-error-rate of a communication system by orders of magnitude by\ntransmitting a well-designed perturbation signal over the channel. We reveal\nthat the adversarial attacks are more destructive than jamming attacks. We also\nshow that classical coding schemes are more robust than autoencoders against\nboth adversarial and jamming attacks. The codes are available at [1].","url_abs":"http://arxiv.org/abs/1902.08391v1","url_pdf":"http://arxiv.org/pdf/1902.08391v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"physical-adversarial-attacks-against-end-to","repo_url":"https://github.com/meysamsadeghi/Security-and-Robustness-of-Deep-Learning-in-Wireless-Communication-Systems","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}