Trojan Scanner: Detecting Hardware Trojans with Rapid SEM Imaging Combined with Image Processing and Machine Learning

Abstract

Hardware Trojans are malicious changes to the design of integrated circuits (ICs) at different stages of the design and fabrication processes. Different approaches have been developed to detect Trojans namely non-destructive (electrical tests like run-time monitoring, functional and structural tests) and destructive (full chip reverse engineering). However, these methods cannot detect all types of Trojans and they suffer from a number of disadvantages such as slow speed of detection and lack of confidence in detecting all types of Trojans. Majority of hardware Trojans implemented in an IC will leave a footprint at the doping (active) layer. In this paper, we introduce a new version of our previously developed “Trojan Scanner” [1] framework for the untrusted foundry threat model, where a trusted GDSII layout (golden layout) is available. Advanced computer vision algorithms in combination with the supervised machine-learning model are used to classify different features of the golden layout and SEM images from an IC under authentication, as a unique descriptor for each type of gates. These descriptors are compared with each other to detect any subtle changes on the active region, which can raise the flag for the existence of a potential hardware Trojan. The descriptors can differentiate variation due to fabrication process, defects, and common SEM image distortions to rule out the possibility of false detection. Our results demonstrate that Trojan Scanner is more reliable than electrical testing and faster than full chip reverse engineering. Trojan Scanner does not rely on the functionality of the circuit rather focuses on the real physical structure to detect malicious changes inserted by the untrusted foundry.

Logo Classification and Data Augmentation Techniques for PCB Assurance and Counterfeit Detection

“…A comprehensive overview of Trojans in PCBs is provided in [16]. There is a multitude of literature that immerse into other methods of Trojan detection [17,18]. [18] presents a survey of various older methods of detecting Trojans, whereas [17] presents a variety of newer methods, but with a focus on using SEM images to detect Trojans in ICs.…”

“…There is a multitude of literature that immerse into other methods of Trojan detection [17,18]. [18] presents a survey of various older methods of detecting Trojans, whereas [17] presents a variety of newer methods, but with a focus on using SEM images to detect Trojans in ICs. As mentioned in [4], ICs are the most frequently counterfeit components but also the most important ones.…”

ViTaL: Verifying Trojan-Free Physical Layouts through Hardware Reverse Engineering

“…This work can be extended by considering the difficulties of a real RE process beyond a simulation of the same. Another related work is the contribution from Vashishta et al [10]. In their Trojan Scanner, computer vision algorithms for feature extraction are combined with a supervised machine learning model.…”

An Effective Clustering Algorithm for the Low-Quality Image of Integrated Circuits via High-Frequency Texture Components Extraction

“…The histogram of oriented gradient (HOG) and support vector machine (SVM) are combined for HT detection [17]. In [18], a "Trojan Scanner" framework is proposed for the untrusted foundry threat model while the trusted golden layout is available. Moreover, deep learning-based techniques attract a lot of attention in the field of IC design and analysis [19][20][21][22].…”