A Reconstruction-Based Defense Framework for Automatic Modulation Recognition

Modulation recognition is critical in intelligent wireless communication, yet deep learning-based automatic modulation classification (AMC) models are vulnerable to adversarial attacks, posing severe risks. While adversarial detection and training offer partial mitigation, they suffer from evasion risks, signal distortion, or high latency—making them unfit for real-time systems like Unmanned Aerial Vehicle (UAV) swarms. Although generative models can purify adversarial inputs, their slow inference limits practicality. Conversely, reconstruction-based methods enable low-latency recovery but often compromise waveform fidelity. We propose a reconstruction-driven adversarial purification approach that directly restores clean signals at the input level, preserving both semantic features and physical consistency without classifier modification, ensuring high accuracy and real-time robustness. Experimental results on the RML2016.10b dataset show that our reconstruction-based methodSigReconstructionachieves an average classification accuracy of 77.04% under adversarial attacks(clean accuracy of 86.68%). Reconstruction quality is corroborated by low mean squared erro (MSE) (0.0286/0.0031/0.0272) and low Fréchet Inception Distance (FID) (62.33/128.82/157.48), indicating faithful waveform recovery and feature alignment. These results demonstrate that targeted reconstruction with physical constraints offers practical, low-latency robustness for adversarially challenged wireless communications.