Sub-Pixel Hardware Trojan Detection With Enhanced Accuracy, Robustness, and Efficiency via Noise-Informed Thermal Radiation Analysis

Hardware Trojans (HTs) pose a significant and growing threat to the field of hardware security. Several side-channel techniques, including power and electromagnetic radiation (EMR), have been proposed for HT detection, constrained by reliance on the golden chip or test vectors. In response, researchers advocate for the use of thermal radiation (TR) to identify HTs. However, existing TR-based methods are designed for the ideal HT that can fully occupy at least one pixel on the thermal radiation map (TRM). In reality, HTs may occupy multiple pixels, substantially diminishing occupancy in each pixel, thereby reducing the detection accuracy of existing methods reliant on the traditional statistical analysis. This challenge is exacerbated by the noise caused by the thermal camera. To this end, this paper introduces a countermeasure named <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</u>oise based p<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</u>xel o<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</u>cupation <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u>nhancement (NICE), aiming to improve the ability of TR-based HT detection. The key insight of NICE is that noise can vary the pixel occupation of HTs while disrupting HT detection. Consequently, the noise can be exploited to statistically find out the largest pixel occupation among the variations, thereby enhancing HT detection accuracy. Subsequently, the Gaussian Mixture Model (GMM) and Isolation Forest (iForest) algorithms are integrated into the NICE framework to identify potential HT pixels based on the distinctive distribution characteristics of TR data, enabling efficient and robust HT detection. Experimental results on a 0.13<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\mu }$</tex-math></inline-formula>m Digital Signal Processing (DSP) show that the detection rate of NICE exceeds the existing TR-based method by 50%, reaching 95%, while maintaining a false alarm rate of 5%. Both metrics of NICE are comparable to the existing power-based and EMR-based methods, eliminating the need for the golden chip and test vectors.