基于模型的深度学习通信信号鲁棒识别算法

Model-based Robust Recognition Algorithm for Deep Learning Communication Signals

深度学习现在是计算机视觉和自然语言处理的热门话题。在许多应用中,深度神经网络(DNN)的性能都优于传统的方法,并且已经成功应用于调制分类和无线电信号表示等任务的学习。近几年研究发现深度神经网络极易受到对抗性攻击,对“对抗性示例”缺乏鲁棒性。笔者就神经网络的通信信号识别算法的鲁棒性问题,将经过PGD攻击的数据看作基于模型的数据,将该数据输入神经网络,使得信号识别分类结果错误;然后借助基于模型的防御算法,即鲁棒训练算法和对抗训练算法,进行训练后实验结果表明,两种方法都具有较好的防御效果。

Deep learning is a hot topic in computer vision and natural language processing.In many applications,the performance of deep neural networks(DNN)is better than traditional methods,and has been successfully applied to tasks such as modulation classification and radio signal representation learning.In recent years,studies have found that deep neural networks are vulnerable to adversarial attacks and lack robustness to"adversarial examples".Regarding the robustness of the neural network communication signal recognition algorithm,the author regards the data that has been attacked by the PGD as model-based data,and inputs the data into the neural network,which makes the signal recognition and classification results wrong.Then with the help of model-based defense algorithms,which are robust training algorithm and adversarial training algorithm,the experimental results after training show that both methods have good defense effects.