Key result
A deep learning model trained with theoretically generated pseudo leakage signals successfully restored actual video display unit leakage signals, demonstrating potential for side-channel attacks.
Why the study?
Collecting real training data poses a challenge for developing deep learning models targeting video display unit side-channel electromagnetic leakage attacks.
Deep learning models trained on theoretically generated pseudo leakage signals can effectively restore actual video display unit leakage signals for side-channel attacks.
This study analyzes the phenomenon of electromagnetic (EM) leakage that occurs through cables and explores the potential for information forensics using deep learning-based image-processing algorithms. We focus on the transition-minimized differential signaling (TMDS) interface to analyze information leakage caused by the inherent differential signal synchronization errors in video graphics controllers (VGC). Our analysis includes detailed mathematical modeling of the EM leakage phenomena from the video display unit (VDU) interface that uses the TMDS protocol. Furthermore, this study presents mathematical models for distortions and alterations caused by the VDU characteristics and its associated RF front-end system. Utilizing mathematical models of EM phenomena, this paper presents a method for creating training datasets for deep learning-based signal processing algorithms by generating and augmenting pseudo leakage signals (PLS) that closely resemble actual leakage signals. This study confirms the practical utility of signal enhancement models trained with generated and augmented PLS in real-world scenarios. Validation involves applying the trained model to measured actual VDU leakage signals and evaluating the results using image quality metrics: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and the structural similarity index measure (SSIM). Ultimately, this study demonstrates the potential to develop deep learning models using theoretically generated PLS for VDU-targeted side-channel attacks, where collecting real training data poses a challenge. This suggests the potential for expanding into high-performance deep learning algorithms in future developments.
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Nam et al. (2024) studied Electromagnetic leakage in video display units. Deep learning-based signal processing algorithms trained with generated and augmented pseudo leakage signals (PLS) was evaluated on Image quality metrics: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR), and the structural similarity index measure (SSIM). A deep learning model trained with theoretically generated pseudo leakage signals successfully restored actual video display unit leakage signals, demonstrating potential for side-channel attacks.
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