Analyzing unintentional electromagnetic (EM) emissions from contemporary devices remains a significant challenge due to the difficulty of identifying potential sources of vulnerability within modern integrated circuit design and the limited research on critical leakage points. These challenges are particularly pressing in today’s information-driven society, where such emissions pose substantial security risks. To address this issue, this paper focuses on spread spectrum clocking (SSC) schemes employed in information visualization devices (IVDs), offering an in-depth analysis of the diverse characteristics of EM leakage and highlighting their associated risks. To convey our contributions, we introduce a novel model based on a modified Fourier series that accurately captures SSC-induced EM waves, enhancing the understanding of emissions from SSC-based devices. Additionally, we propose complete coherent demodulation (CCD), an advancement of the periodic nonuniform sampling (PNS) framework that resolves byproduct-phase terms. Our work further integrates parameterized demodulation with singular value decomposition (PD-SVD) to refine the analysis of modulated instantaneous frequency terms within EM leakage observed over significant distances. These contributions advance security assessments and strengthen electromagnetic resilience.

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.