A novel wavelet based independent component analysis method for pre-processing computer video leakage signal

Abstract

Computer displays emit electromagnetic waves, which compromise the information displayed by the computer. This can be a potential information security threat as the sensitive information can be stolen from a distance without leaving any trace. The video leakage signals contain the information of the image displayed in the computer, so the video leakage signals can be seen as special image signals. However, different from the normal image signal, the signal to noise ratio (SNR) of video leakage signal is low due to the environmental noise and many other man-made noises. In this paper, a novel wavelet based independent component analysis (ICA) method is proposed for improving SNR of computer video leakage signals. By using this method, we can improve the performance of pre-processing of computer video leaking signals. We solve the problem of using Fast ICA in processing video leakage signal by using a wavelet filter. The performance of Fast ICA is improved by working in wavelet domain because of advantages like ease of implementation and less computation time when compared to time domain. We pre-process one-dimensional received signal without reconstructing the image since it is inefficient to reconstruct signal before processing. A direct SNR can't be defined. Therefore, another metric called quasi signal to noise ratio (QSNR) is defined to estimate signal to noise ratio of video leakage signals. The processed results of the actual experimental data show that the proposed wavelet based ICA algorithm has a better performance than the Fast ICA algorithm and the Wavelet denoising algorithm.