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Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7599-4367
2019 (English)In: IEEE Communications Letters, ISSN 1089-7798, E-ISSN 1558-2558, Vol. 23, no 5, p. 847-850Article in journal (Refereed) Published
Abstract [en]

We show that end-to-end learning of communication systems through deep neural network autoencoders can be extremely vulnerable to physical adversarial attacks. Specifically, we elaborate how an attacker can craft effective physical black-box adversarial attacks. Due to the openness (broadcast nature) of the wireless channel, an adversary transmitter can increase the block-error-rate of a communication system by orders of magnitude by transmitting a well-designed perturbation signal over the channel. We reveal that the adversarial attacks are more destructive than the jamming attacks. We also show that classical coding schemes are more robust than the autoencoders against both adversarial and jamming attacks.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 23, no 5, p. 847-850
Keywords [en]
Adversarial attacks; autoencoder systems; deep learning; wireless security; end-to-end learning
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:liu:diva-157535DOI: 10.1109/LCOMM.2019.2901469ISI: 000467552900019OAI: oai:DiVA.org:liu-157535DiVA, id: diva2:1328681
Note

Funding Agencies|Swedish Foundation for Strategic Research (SSF); Security-Link

Available from: 2019-06-22 Created: 2019-06-22 Last updated: 2019-10-22

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