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Open-Set RF Fingerprinting via Improved Prototype Learning (2306.13895v1)

Published 24 Jun 2023 in eess.SP and cs.CV

Abstract: Deep learning has been widely used in radio frequency (RF) fingerprinting. Despite its excellent performance, most existing methods only consider a closed-set assumption, which cannot effectively tackle signals emitted from those unknown devices that have never been seen during training. In this letter, we exploit prototype learning for open-set RF fingerprinting and propose two improvements, including consistency-based regularization and online label smoothing, which aim to learn a more robust feature space. Experimental results on a real-world RF dataset demonstrate that our proposed measures can significantly improve prototype learning to achieve promising open-set recognition performance for RF fingerprinting.

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References (27)
  1. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015.
  2. S. Pouyanfar, S. Sadiq, Y. Yan, H. Tian et al., “A survey on deep learning: Algorithms, techniques, and applications,” ACM Computing Surveys (CSUR), vol. 51, no. 5, pp. 1–36, 2018.
  3. N. Soltanieh, Y. Norouzi, Y. Yang, and N. C. Karmakar, “A review of radio frequency fingerprinting techniques,” IEEE Journal of Radio Frequency Identification, vol. 4, no. 3, pp. 222–233, 2020.
  4. S. Riyaz, K. Sankhe, S. Ioannidis, and K. Chowdhury, “Deep learning convolutional neural networks for radio identification,” IEEE Communications Magazine, vol. 56, no. 9, pp. 146–152, 2018.
  5. J. Yu, A. Hu, G. Li, and L. Peng, “A robust rf fingerprinting approach using multisampling convolutional neural network,” IEEE internet of things journal, vol. 6, no. 4, pp. 6786–6799, 2019.
  6. K. Sankhe, M. Belgiovine, F. Zhou, L. Angioloni et al., “No radio left behind: Radio fingerprinting through deep learning of physical-layer hardware impairments,” IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 165–178, 2019.
  7. Y. Zou, J. Zhu, X. Wang, and L. Hanzo, “A survey on wireless security: Technical challenges, recent advances, and future trends,” Proceedings of the IEEE, vol. 104, no. 9, pp. 1727–1765, 2016.
  8. W. J. Scheirer, A. de Rezende Rocha, A. Sapkota, and T. E. Boult, “Toward open set recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 7, pp. 1757–1772, 2012.
  9. A. Bendale and T. E. Boult, “Towards open set deep networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 1563–1572.
  10. Z. Ge, S. Demyanov, Z. Chen, and R. Garnavi, “Generative openmax for multi-class open set classification,” arXiv preprint arXiv:1707.07418, 2017.
  11. L. Neal, M. Olson, X. Fern, W.-K. Wong, and F. Li, “Open set learning with counterfactual images,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 613–628.
  12. R. Yoshihashi, W. Shao, R. Kawakami, S. You, M. Iida, and T. Naemura, “Classification-reconstruction learning for open-set recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 4016–4025.
  13. X. Sun, Z. Yang, C. Zhang, K.-V. Ling, and G. Peng, “Conditional gaussian distribution learning for open set recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 13 480–13 489.
  14. H.-M. Yang, X.-Y. Zhang, F. Yin, and C.-L. Liu, “Robust classification with convolutional prototype learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3474–3482.
  15. H.-M. Yang, X.-Y. Zhang, F. Yin, Q. Yang, and C.-L. Liu, “Convolutional prototype network for open set recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 5, pp. 2358–2370, 2020.
  16. G. Chen, P. Peng, X. Wang, and Y. Tian, “Adversarial reciprocal points learning for open set recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 8065–8081, 2021.
  17. J. Lu, Y. Xu, H. Li, Z. Cheng, and Y. Niu, “PMAL: Open set recognition via robust prototype mining,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 2, 2022, pp. 1872–1880.
  18. R. Müller, S. Kornblith, and G. E. Hinton, “When does label smoothing help?” Advances in neural information processing systems, vol. 32, 2019.
  19. Z. Shen, Z. Liu, D. Xu, Z. Chen, K.-T. Cheng, and M. Savvides, “Is label smoothing truly incompatible with knowledge distillation: An empirical study,” arXiv preprint arXiv:2104.00676, 2021.
  20. A. B. Siddik, D. Drake, T. Wilkinson, P. L. De Leon, S. Sandoval, and M. Campos, “WIDEFT: A corpus of radio frequency signals for wireless device fingerprint research,” in 2021 IEEE International Symposium on Technologies for Homeland Security (HST).   IEEE, 2021, pp. 1–7.
  21. K. Sohn, D. Berthelot, C.-L. Li et al., “FixMatch: Simplifying semi-supervised learning with consistency and confidence,” arXiv preprint arXiv:2001.07685, 2020.
  22. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818–2826.
  23. B. Dubuisson and M. Masson, “A statistical decision rule with incomplete knowledge about classes,” Pattern recognition, vol. 26, no. 1, pp. 155–165, 1993.
  24. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  25. W. Wang, C. Luo, J. An, L. Gan et al., “Semi-supervised RF fingerprinting with consistency-based regularization,” arXiv preprint arXiv:2304.14795, 2023.
  26. M. Abadi, A. Agarwal, P. Barham et al., “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/
  27. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.

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