In this article, we present results on research performed in the TEMPEST domain, which studies the electromagnetic disturbances generated unintentionally by electronic equipment as well as the methods to protect the information processed by this equipment against these electromagnetic phenomena. The highest vulnerability of information leakage is attributed to the display video signal from the TEMPEST domain perspective. Examples of far-range propagation on a power line of this type of disturbance will be illustrated for the first time. Thus, the examples will highlight the possibility of recovering processed information at distances of 1, 10 and 50 m. There are published articles studying electromagnetic disturbances generated by electronic equipment propagating on power cables of such equipment but no studies on their long-distance propagation. Our research aims to raise awareness in the scientific community and the general public of the existence of such vulnerabilities that can compromise confidential or sensitive information that can make the difference between success or failure in the business sector, for example, or can harm personal privacy, which is also important for us all. Countermeasures to reduce or even eliminate these threats will also be presented based on the analysis of the signal-to noise-ratio recorded during our research.

The information technology and communication (IT&C) market consists of computing and telecommunication technology systems, which also include a variety of audio devices. Preserving the confidentiality of transmitted information through these devices stands as a critical concern across various domains and businesses. Notably, spurious electromagnetic emanations emitted by audio devices can be captured and processed, potentially leading to eavesdropping incidents. The evaluation of electronic devices for potential security vulnerabilities often involves employing Transient Electromagnetic Pulse Emanation Standard (TEMPEST) technology. This paper introduces a novel approach to TEMPEST testing specifically tailored for audio devices. The outcomes of the proposed approach offer valuable insights into TEMPEST equipment testing, aiming to mitigate the potential risks posed by threats exploitable by eavesdroppers in everyday scenarios. The present work delves into the examination of two ubiquitous global electronic devices: a notebook and a pair of in-ear headphones. The symmetrical framework of this study arises from the intrinsic similarity that, despite belonging to distinct categories, both devices possess the capability to emit electromagnetic emissions that contain compromised audio signals. This assertion is substantiated by the measurement results presented herein. The proposed methodology involves the analysis of the audio amplitude modulation (AM) demodulated signal in the frequency domain. This innovative approach not only mitigates operator fatigue but also significantly reduces the testing time required for these devices and instrument running hours and leads to the development of new applications.

Analysis of unintended compromising emissions from Video Display Units (VDUs) is an important topic in research communities. This paper examines the feasibility of recovering the information displayed on the monitor from reconstructed video frames. The study holds particular significance for our understanding of security vulnerabilities associated with the electromagnetic radiation of digital displays. Considering the amount of noise that reconstructed TEMPEST video frames have, the work in this paper focuses on two different approaches to de-noising images for efficient optical character recognition. First, an Adaptive Wiener Filter (AWF) with adaptive window size implemented in the spatial domain was tested, and then a Convolutional Neural Network (CNN) with an encoder–decoder structure that follows both classical auto-encoder model architecture and U-Net architecture (auto-encoder with skip connections). These two techniques resulted in an improvement of more than two times on the Structural Similarity Index Metric (SSIM) for AWF and up to four times for the SSIM for the Deep Learning (DL) approach. In addition, to validate the results, the possibility of text recovery from processed noisy frames was studied using a state-of-the-art Tesseract Optical Character Recognition (OCR) engine. The present work aims to bring to attention the security importance of this topic and the non-negligible character of VDU information leakages.