Adapt under Attack and Domain Shift: Unified Adversarial Meta-Learning and Domain Adaptation for Robust Automatic Modulation Classification

TL;DR

This paper introduces a unified meta-learning and domain adaptation framework to enhance the robustness of automatic modulation classification systems against adversarial attacks and environmental shifts, improving real-world deployment viability.

Contribution

It presents a novel integrated approach combining meta-learning and domain adaptation to defend against adversarial attacks and adapt to new environments in AMC systems.

Findings

Significant accuracy improvement against adversarial attacks.

Effective adaptation to new target domains without extensive labeled data.

Enhanced robustness of AMC systems in dynamic environments.

Abstract

Deep learning has emerged as a leading approach for Automatic Modulation Classification (AMC), demonstrating superior performance over traditional methods. However, vulnerability to adversarial attacks and susceptibility to data distribution shifts hinder their practical deployment in real-world, dynamic environments. To address these threats, we propose a novel, unified framework that integrates meta-learning with domain adaptation, making AMC systems resistant to both adversarial attacks and environmental changes. Our framework utilizes a two-phase strategy. First, in an offline phase, we employ a meta-learning approach to train the model on clean and adversarially perturbed samples from a single source domain. This method enables the model to generalize its defense, making it resistant to a combination of previously unseen attacks. Subsequently, in the online phase, we apply domain adaptation to align the model's features with a new target domain, allowing it to adapt without requiring substantial labeled data. As a result, our framework achieves a significant improvement in modulation classification accuracy against these combined threats, offering a critical solution to the deployment and operational challenges of modern AMC systems.