Abstract
Deep learning (DL) based communication system is a promising novel architecture to implement end-to-end optimization compared with conventional block-separated optimization schemes. However, the vulnerability to adversarial examples of deep neural networks poses significant security concern on the end-to-end communication systems. Adversarial attacks serve as a fundamental surrogate to evaluate the robustness of the DL-based communication systems before they are deployed. Specifically, we propose a new adversarial attack method with momentum iterative gradient against the end-to-end communication systems. For targeted attacks, embedding the momentum term in the iterative process can help loss function stabilize the update direction and avoid getting stuck in saddle points and poor local minima. Therefore, the momentum-based method can enhance the effectiveness without losing the transferability of adversarial attacks. Numerous simulation results illustrate that the proposed method can achieve superior block error rate compared with traditional jamming attacks and no momentum accumulated adversarial attacks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
O’Shea, T., Hoydis, J.: An introduction to deep learning for the physical layer. IEEE Trans. Cognit. Commun. Netw. 3(4), 563–575 (2017). https://doi.org/10.1109/TCCN.2017.2758370
Dörner, S., Cammerer, S., Hoydis, J., Brink, S.t.: Deep learning based communication over the air. IEEE J. Sel. Top. Signal Process. 12(1), 132–143 (2018). https://doi.org/10.1109/JSTSP.2017.2784180
Ye, H., Liang, L., Li, G.Y., Juang, B.H.: Deep learning-based end-to-end wireless communication systems with conditional gans as unknown channels. IEEE Trans. Wirel. Commun. 19(5), 3133–3143 (2020). https://doi.org/10.1109/TWC.2020.2970707
Chen, X., Cheng, J., Zhang, Z., Wu, L., Dang, J., Wang, J.: Data-rate driven transmission strategies for deep learning-based communication systems. IEEE Trans. Commun. 68(4), 2129–2142 (2020). https://doi.org/10.1109/TCOMM.2020.2968314
Ye, H., Li, G.Y., Juang, B.H.F.: Deep learning based end-to-end wireless communication systems without pilots. IEEE Trans. Cognit. Commun. Netw. 1 (2021). https://doi.org/10.1109/TCCN.2021.3061464
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)
Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)
Sadeghi, M., Larsson, E.G.: Physical adversarial attacks against end-to-end autoencoder communication systems. IEEE Commun. Lett. 23(5), 847–850 (2019). https://doi.org/10.1109/LCOMM.2019.2901469
Sadeghi, M., Larsson, E.G.: Adversarial attacks on deep-learning based radio signal classification. IEEE Wirel. Commun. Lett. 8(1), 213–216 (2019). https://doi.org/10.1109/LWC.2018.2867459
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT press, Cambridge (2016)
Polyak, B.T.: Some methods of speeding up the convergence of iteration methods. USSR Comput. Math. Math. Phys. 4(5), 1–17 (1964)
Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9185–9193 (2018)
Duch, W., Korczak, J.: Optimization and global minimization methods suitable for neural networks. Neural Comput. Surv. 2, 163–212 (1998)
Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning, pp. 1139–1147. PMLR (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhang, Q., Ma, Y., Zhao, H., Shan, C., Zhang, J. (2022). Momentum-Based Adversarial Attacks Against End-to-End Communication Systems. In: Guo, Q., Meng, W., Jia, M., Wang, X. (eds) Wireless and Satellite Systems. WiSATS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 410. Springer, Cham. https://doi.org/10.1007/978-3-030-93398-2_48
Download citation
DOI: https://doi.org/10.1007/978-3-030-93398-2_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93397-5
Online ISBN: 978-3-030-93398-2
eBook Packages: Computer ScienceComputer Science (R0)