一种多任务注意力联合蒸馏的调制识别方法OA

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.