Long Range Detection of Emanation from HDMI Cables Using CNN and Transfer Learning

The transition of data and clock signals between high and low states in electronic devices creates electromagnetic radiation according to Maxwell's equations. These unintentional emissions, called emanation, may have a significant correlation with the original information-carrying signal and form an information leakage source, bypassing secure cryptographic methods at both hardware and software levels. Information extraction exploiting compromising emanations poses a major threat to information security. Shielding the devices and cables along with setting a control perimeter for a sensitive facility are the most commonly used preventive measures. These countermeasures raise the research need for the longest detection range of exploitable emanation and the efficacy of commercial shielding. In this work, using data collected from 3 types of commercial HDMI cables (unshielded, single-shielded, and double-shielded) in an office environment, we have shown that the CNN-based detection method outperforms the traditional threshold-based detection method and improves the detection range from 4 m to 22.5 m for an iso-accuracy of ~ 95%. Also, for an iso-distance of 16 m, the CNN-based method provides ~ 100% accuracy, compared to ~ 88.5% using the threshold-based method. The significant performance boost is achieved by treating the FFT plots as images and training a residual neural network (ResNet) with the data so that it learns to identify the impulse-like emanation peaks even in the presence of other interfering signals. A comparison has been made among the emanation power from the 3 types of HDMI cables to judge the efficacy of multi-layer shielding. Finally, a distinction has been made between monitor contents, i.e., still image vs video, with an accuracy of 91.7% at a distance of 16 m. This distinction bridges the gap between emanation-based image and video reconstruction algorithms.