mlsanityData to 7 Oct 2026

A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attacks

arXiv 2103.02654 ↗First posted 3 Mar 2021cs.LG cs.AI eess.SPcited by 0

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Abstract (arXiv)

We propose a novel defensive mechanism based on a generative adversarial network (GAN) framework to defend against adversarial attacks in end-to-end communications systems. Specifically, we utilize a generative network to model a powerful adversary and enable the end-to-end communications system to combat the generative attack network via a minimax game. We show that the proposed system not only works well against white-box and black-box adversarial attacks but also possesses excellent generalization capabilities to maintain good performance under no attacks. We also show that our GAN-based end-to-end system outperforms the conventional communications system and the end-to-end communications system with/without adversarial training.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 19 references that are in the map. First posted on arXiv 3 Mar 2021.

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15 of 19 references.

References · 15

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2013Intriguing properties of neural networkscited by 5,114
  4. 2016Adversarial Machine Learning at Scalecited by 934
  5. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
  6. 2018Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Modelscited by 362
  7. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 350
  8. 2017An Introduction to Deep Learning for the Physical Layercited by 227
  9. 2019Adversarial Training and Robustness for Multiple Perturbationscited by 182
  10. 2019Adversarial Training Can Hurt Generalizationcited by 131
  11. 2017The Numerics of GANscited by 104
  12. 2018Adversarial Attacks on Deep-Learning Based Radio Signal Classificationcited by 47
3 more
  1. 2019Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systemscited by 19
  2. 2019A Direct Approach to Robust Deep Learning Using Adversarial Networkscited by 18
  3. 2020Adversarial Attack on DL-based Massive MIMO CSI Feedbackcited by 9

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