arXiv Machine Learning October 28, 2026 By Shiwei Liu, Linyuan Wang, Nuolin Sun, Libin Hou, Bin Yan

A JiT-Based Denoising Defense Method Against Adversarial Examples for Radio Signal Automatic Modulation Classification

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The paper introduces a JiT-based denoising defense for radio signal automatic modulation classification, employing a 1D Transformer trained via flow matching to map adversarial examples onto a clean signal manifold. Experiments on the RML2018.01A dataset show the method outperforms PGD-based adversarial training, improving robust accuracy by at least 20.58% under PGD attacks and clean accuracy by 10.20%. It also demonstrates stronger defense against unseen attack types, achieving up to a 39.72% improvement in robust accuracy at higher attack strengths.

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