基于深度学习的计算机显示器电磁信息泄漏识别

本文以计算机显示设备泄漏电磁信号为研究对象,对于人工提取特征识别电磁泄漏信号存在的主观性强、特征冗余的问题,区别于传统基于经验的人工特征提取模式,利用人工智能深度学习方法,使用处理图像的深度学习技术应用于电磁信息泄漏特征识别,提出了一种基于卷积神经网络的识别方法.该方法首先提取电磁泄漏信号的时频谱信息作为卷积神经网络模型的输入,然后利用模型的自学习能力提取深层特征,实现对不同分辨率来源电磁泄漏信号的识别,识别准确率达到98％,单信号检测时间仅需40 ms,验证了卷积神经网络应用于电磁泄漏信号识别的有效性,为电磁泄漏预警与防护提供了重要依据,为电磁泄漏视频信号还原复现提供有力支撑.

Electromagnetic Information Leakage Recognition of Computer Display Based on Deep Learning

The electromagnetic leakage signals recognized by manually extracted features are strongly subjective with feature redundancy. For this reason, different from the traditional artificial feature extraction mode based on experience, this study proposes a recognition method based on a Convolutional Neural Network (CNN), with the electromagnetic leakage signals of computer displays as the research object. This method employs the artificial intelligence-based deep learning method and applies the deep learning technology of image processing to the leakage feature recognition of electromagnetic information. Firstly, the time-frequency spectrum information of electromagnetic leakage signals is extracted as the input of the CNN model. Then, the deep-seated features are extracted by the self-learning ability of the model to recognize electromagnetic leakage signals from sources with different resolutions. Finally, the recognition accuracy reaches 98%, and the detection of a single signal only takes 40 ms, which verifies the effectiveness of CNN in the recognition of electromagnetic leakage signals. The proposed method provides an important basis for the early warning and protection of electromagnetic leakage and offers strong support to the restoration and reproduction of electromagnetic leakage video signals.