电讯技术2026，Vol.66Issue(5)：782-790,9.DOI:10.20079/j.issn.1001-893x.241218001
一种多任务注意力联合蒸馏的调制识别方法
A Multi-task Attention Joint Knowledge Distillation Method for Modulation Recognition
摘要
Abstract
In the field of intelligent modulation signal recognition,neural networks exhibit poor defense performance when subjected to adversarial attacks.To address this challenge,a multi-task attention-based joint distillation training framework built upon traditional neural network models is proposed.The framework separately trains two high-quality teacher models specifically designed for clean samples and adversarial examples respectively,which then jointly guide the training of the student network through knowledge distillation.Notably,an attention mechanism is introduced to dynamically balance the training losses between clean and adversarial samples during the joint optimization process.This innovative architecture ultimately constructs a student network demonstrating enhanced defensive capabilities against adversarial attacks while maintaining strong robustness in normal classification scenarios,achieving optimal equilibrium between security protection and model stability.Experimental results demonstrate that the proposed method effectively defends against adversarial signals while maintaining recognition accuracy for clean sample signals.Compared with traditional adversarial training methods,the proposed method improves the recognition accuracy for clean samples by approximately 10%.Furthermore,when facing different modulation recognition attack methods and varying intensities of perturbation attacks,the proposed approach exhibits good generalization and robust defense degradation.关键词
调制识别/深度神经网络/对抗训练/知识蒸馏/注意力机制Key words
modulation recognition/deep neural network/adversarial training/knowledge distillation/attention mechanism分类
信息技术与安全科学引用本文复制引用
颜祥俊,陈奕功,刘少龙,尚志会,张涛..一种多任务注意力联合蒸馏的调制识别方法[J].电讯技术,2026,66(5):782-790,9.基金项目
国家自然科学基金资助项目(62371463) （62371463）
