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Multiuser adversarial attack on deep learning for OFDM detection
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Ye, Youjie, Chen, Yunfei and Liu, Mingqian (2022) Multiuser adversarial attack on deep learning for OFDM detection. IEEE Wireless Communications Letters, 11 (12). pp. 2527-2531. doi:10.1109/LWC.2022.3207348 ISSN 2162-2337.
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WRAP-multiuser-adversarial-attack-deep-learning-OFDM-detection-Chen-2022.pdf - Accepted Version - Requires a PDF viewer. Download (1218Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/LWC.2022.3207348
Abstract
Adversarial attack has been widely used to degrade the performance of deep learning (DL), especially in the field of communications. In this letter, we evaluate different white-box and black-box adversarial attack algorithms for a DL-based multiuser orthogonal frequency division multiplexing (OFDM) detector subject to multiuser adversarial attack. The bit error rates under different adversarial attacks are compared. The results show that, the perturbation efficiency of adversarial attack is higher than conventional multiuser interference. Virtual adversarial methods (VAM) and zeroth-order-optimization (ZOO) attacks perform the best among white-box and black-box methods, respectively. They are also effective when the attack changes the starting time. Additionally, adding the number of attackers is found useful to improve the VAM attack but not for ZOO. This work shows that adversarial attack is powerful to generate adversarial against multiuser OFDM communications.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Library of Congress Subject Headings (LCSH): | Wireless communication system, Orthogonal frequency division multiplexing, Radio - Transmitter-receivers, Computer security, Deep learning (Machine learning) | ||||||||
| Journal or Publication Title: | IEEE Wireless Communications Letters | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 2162-2337 | ||||||||
| Official Date: | December 2022 | ||||||||
| Dates: |
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| Volume: | 11 | ||||||||
| Number: | 12 | ||||||||
| Page Range: | pp. 2527-2531 | ||||||||
| DOI: | 10.1109/LWC.2022.3207348 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2022 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Date of first compliant deposit: | 31 October 2022 | ||||||||
| Date of first compliant Open Access: | 31 October 2022 | ||||||||
| RIOXX Funder/Project Grant: |
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