The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Abstract
Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks. We consider the use of DL for radio signal (modulation) classification tasks, and present practical methods for the crafting of white-box and universal black-box adversarial attacks in that application. We show that these attacks can considerably reduce the classification performance, with extremely small perturbations of the input. In particular, these attacks are significantly more powerful than classical jamming attacks, which raises significant security and robustness concerns in the use of DL-based algorithms for the wireless physical layer.
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Explaining and Harnessing Adversarial Examples
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) · arXiv · doi:10.48550/arxiv.1412.6572 · Cited by 10,381
Intriguing properties of neural networks
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Universal Adversarial Perturbations
Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., & Frossard, P. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.17 · Cited by 2,680
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
Over-the-Air Deep Learning Based Radio Signal Classification
O'Shea, T. J., Roy, T., & Clancy, T. C. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2018.2797022 · Cited by 1,607
Convolutional Radio Modulation Recognition Networks
O’Shea, T. J., Corgan, J., & Clancy, T. C. (2016) · Communications in Computer and Information Science · doi:10.1007/978-3-319-44188-7_16 · Cited by 1,584
Learning to optimize: Training deep neural networks for wireless resource management
Sun, H., Chen, X., Shi, Q., Hong, M., Fu, X., & Sidiropoulos, N. D. (2017) · 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · doi:10.1109/spawc.2017.8227766 · Cited by 308
Radio Machine Learning Dataset Generation with GNU Radio
O’Shea, T. J. (2025) · TIB Data Manager · doi:10.57702/h54kawox · Cited by 303
An Iterative BP-CNN Architecture for Channel Decoding
Liang, F., Shen, C., & Wu, F. (2018) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2018.2794062 · Cited by 276
Cited by
Machine learning in NextG networks via generative adversarial networks
Ayanoglu, E., Davaslioglu, K., & Sagduyu, Y. E. (2022) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2022.3153004 · Cited by 55
Metadata
- Authors
- Sadeghi, M., & Larsson, E. G.
- Venue
- IEEE Wireless Communications Letters
- Published
- 2019-02
- Volume
- 8
- Issue
- 1
- Pages
- 213-216
- ISSN
- 2162-2337 (ISSN-L)
- 2162-2345
- OpenAlex
- W2888024549
- Cited by
- 346
- References
- 10
- FWCI
- 14.99
- Type
- journal article
- Language
- en
- Open access
- closed
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