Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification

Amirmohammad Bamdad, Ali Owfi, Fatemeh Afghah

TL;DR

The paper tackles the vulnerability of DL-based automatic modulation classification (AMC) to adversarial perturbations, especially unseen attacks, which hinders real-time deployment. It introduces a meta-learning-based adversarial training framework that performs offline meta-training across diverse attack scenarios to learn a generalized adaptation strategy, enabling fast online fine-tuning with few samples via inner-loop updates (MAML and alternatives). The approach yields improved robustness to unseen, especially black-box, attacks and significantly reduces online data and computation needs, while increasing offline training time; it is compatible with any backbone network. Practically, this enables robust AMC in dynamic wireless environments where training data and resources are limited.

Abstract

DL-based automatic modulation classification (AMC) models are highly susceptible to adversarial attacks, where even minimal input perturbations can cause severe misclassifications. While adversarially training an AMC model based on an adversarial attack significantly increases its robustness against that attack, the AMC model will still be defenseless against other adversarial attacks. The theoretically infinite possibilities for adversarial perturbations mean that an AMC model will inevitably encounter new unseen adversarial attacks if it is ever to be deployed to a real-world communication system. Moreover, the computational limitations and challenges of obtaining new data in real-time will not allow a full training process for the AMC model to adapt to the new attack when it is online. To this end, we propose a meta-learning-based adversarial training framework for AMC models that substantially enhances robustness against unseen adversarial attacks and enables fast adaptation to these attacks using just a few new training samples, if any are available. Our results demonstrate that this training framework provides superior robustness and accuracy with much less online training time than conventional adversarial training of AMC models, making it highly efficient for real-world deployment.

Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification

TL;DR

The paper tackles the vulnerability of DL-based automatic modulation classification (AMC) to adversarial perturbations, especially unseen attacks, which hinders real-time deployment. It introduces a meta-learning-based adversarial training framework that performs offline meta-training across diverse attack scenarios to learn a generalized adaptation strategy, enabling fast online fine-tuning with few samples via inner-loop updates (MAML and alternatives). The approach yields improved robustness to unseen, especially black-box, attacks and significantly reduces online data and computation needs, while increasing offline training time; it is compatible with any backbone network. Practically, this enables robust AMC in dynamic wireless environments where training data and resources are limited.

Abstract

DL-based automatic modulation classification (AMC) models are highly susceptible to adversarial attacks, where even minimal input perturbations can cause severe misclassifications. While adversarially training an AMC model based on an adversarial attack significantly increases its robustness against that attack, the AMC model will still be defenseless against other adversarial attacks. The theoretically infinite possibilities for adversarial perturbations mean that an AMC model will inevitably encounter new unseen adversarial attacks if it is ever to be deployed to a real-world communication system. Moreover, the computational limitations and challenges of obtaining new data in real-time will not allow a full training process for the AMC model to adapt to the new attack when it is online. To this end, we propose a meta-learning-based adversarial training framework for AMC models that substantially enhances robustness against unseen adversarial attacks and enables fast adaptation to these attacks using just a few new training samples, if any are available. Our results demonstrate that this training framework provides superior robustness and accuracy with much less online training time than conventional adversarial training of AMC models, making it highly efficient for real-world deployment.
Paper Structure (13 sections, 3 equations, 5 figures, 2 tables, 1 algorithm)

This paper contains 13 sections, 3 equations, 5 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Methodology
  3. Adversarial Attacks
  4. Problem Formulation and Design
  5. Proposed Framework
  6. Results
  7. Dataset Generation
  8. Baseline Models
  9. Evaluations
  10. Adaptation to Unknown Adversarial Attacks
  11. Generalization to Unknown Adversarial Attacks
  12. Sample and Time Efficiency
  13. Conclusion

Figures (5)

  • Figure 1: Limitations of AMC models after being deployed to a practical real-world system (online-phase).
  • Figure 2: Black-box adversarial attack on an AMC models.
  • Figure 3: Proposed meta-learning-based adversarial training framework for AMC models. The numbers in the adversarial meta-learning part denote the order of steps. The adversarial meta-learning section is depicted based on MAML, but theoretically, any model-agnostic meta-learning algorithm can be used as well.
  • Figure 4: Adaptation comparison of AMC baselines towards unseen adversarial attacks in few-shot scenarios. Only a few shots of adversarial training samples from the new tested adversarial attacks are provided to the AMC models.
  • Figure 5: Generalization comparison of AMC baselines against unseen adversarial attacks in 0-shot scenario. No new adversarial training samples from the new tested adversarial attacks are provided to the AMC models.