mlsanityData to 7 Oct 2026

A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification

arXiv 2407.06807 ↗First posted 9 Jul 2024cs.AIcited by 3

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Abstract (arXiv)

Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applying deep neural network for devising future generations of wireless networks. However, several recent works have pointed out that imperceptible and carefully designed adversarial examples (attacks) can significantly deteriorate the classification accuracy. In this paper, we investigate a defense mechanism based on both training-time and run-time defense techniques for protecting machine learning-based radio signal (modulation) classification against adversarial attacks. The training-time defense consists of adversarial training and label smoothing, while the run-time defense employs a support vector machine-based neural rejection (NR). Considering a white-box scenario and real datasets, we demonstrate that our proposed techniques outperform existing state-of-the-art technologies.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 12 of its 15 references that are in the map. First posted on arXiv 9 Jul 2024.

Builds on

12 of 15 references.

References · 12

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2018MMA Training: Direct Input Space Margin Maximization through Adversarial Trainingcited by 171
  4. 2017Over the Air Deep Learning Based Radio Signal Classificationcited by 121
  5. 2020CAT: Customized Adversarial Training for Improved Robustnesscited by 61
  6. 2018Adversarial Attacks on Deep-Learning Based Radio Signal Classificationcited by 47
  7. 2019Fast Deep Learning for Automatic Modulation Classificationcited by 21
  8. 2017Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoidcited by 19
  9. 2019Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training?cited by 18
  10. 2019Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-trainingcited by 7
  11. 2019Classification of Radio Signals and HF Transmission Modes with Deep Learningcited by 7
  12. 2021A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classificationcited by 7

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Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classificationcited by 4
  2. 2025Vision Transformer with Adversarial Indicator Token against Adversarial Attacks in Radio Signal Classificationscited by 2
  3. 2025A Novel and Practical Universal Adversarial Perturbations against Deep Reinforcement Learning based Intrusion Detection Systemscited by 1