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

• URL: http://arxiv.org/abs/2511.01172v1
• Date: Mon, 03 Nov 2025 02:44:53 GMT
• Title: Adapt under Attack and Domain Shift: Unified Adversarial Meta-Learning and Domain Adaptation for Robust Automatic Modulation Classification
• Authors: Ali Owfi, Amirmohammad Bamdad, Tolunay Seyfi, Fatemeh Afghah,
• Abstract summary: We propose a novel, unified framework that integrates meta-learning with domain adaptation.<n>Our framework achieves a significant improvement in modulation classification accuracy against these combined threats.
• Score: 7.380560017792149
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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