Information Importance-Aware Defense Against Adversarial Attack for Automatic Modulation Classification: An XAI-Based Approach

Deep learning (DL) has significantly improved automatic modulation classification (AMC) by leveraging neural networks as the feature extractor. However, as the DL-based AMC becomes increasingly widespread, it is faced with the severe secure issue from various adversarial attacks. Existing defense methods often suffer from the high computational cost, intractable parameter tuning, and insufficient robustness. This paper proposes an eXplainable artificial intelligence (XAI) defense approach, which uncovers the negative information caused by the adversarial attack through measuring the importance of input features based on the SHapley Additive exPlanations (SHAP). By properly removing the negative information in adversarial samples and then fine-tuning(FT) the model, the impact of the attacks on the classification result can be mitigated. Experimental results demonstrate that the proposed SHAP-FT improves the classification performance of the model by 15%~20% under different attack levels, which not only enhances model robustness against various attack levels but also reduces the resource consumption, validating its effectiveness in safeguarding communication networks.