In the context of hardware trust and assurance, reverse engineering has been often considered as an illegal action. Generally speaking, reverse engineering aims to retrieve information from a product, i.e., integrated circuits (ICs) and printed circuit boards (PCBs) in hardware security-related scenarios, in the hope of understanding the functionality of the device and determining its constituent components. Hence, it can raise serious issues concerning Intellectual Property (IP) infringement, the (in)effectiveness of security-related measures, and even new opportunities for injecting hardware Trojans. Ironically, reverse engineering can enable IP owners to verify and validate the design. Nevertheless, this cannot be achieved without overcoming numerous obstacles that limit successful outcomes of the reverse engineering process. This article surveys these challenges from two complementary perspectives: image processing and machine learning. These two fields of study form a firm basis for the enhancement of efficiency and accuracy of reverse engineering processes for both PCBs and ICs. In summary, therefore, this article presents a roadmap indicating clearly the actions to be taken to fulfill hardware trust and assurance objectives.
Reverse engineering (RE) is the only foolproof method of establishing trust and assurance in hardware. This is especially important in today's climate, where new threats are arising daily. A Printed Circuit Board (PCB) serves at the heart of virtually all electronic systems and, for that reason, a precious target amongst attackers. Therefore, it is increasingly necessary to validate and verify these hardware boards both accurately and efficiently. When discussing PCBs, the current state-of-the-art is non-destructive RE through X-ray Computed Tomography (CT); however, it remains a predominantly manual process. Our work in this paper aims at paving the way for future developments in the automation of PCB RE by presenting automatic detection of vias, a key component to every PCB design. We provide a via detection framework that utilizes the Hough circle transform for the initial detection, and is followed by an iterative false removal process developed specifically for detecting vias. We discuss the challenges of detecting vias, our proposed solution, and lastly, evaluate our methodology not only from an accuracy perspective but the insights gained through iteratively removing false-positive circles as well. We also compare our proposed methodology to an off-the-shelf implementation with minimal adjustments of Mask R-CNN; a fast object detection algorithm that, although is not optimized for our application, is a reasonable deep learning model to measure our work against. The Mask R-CNN we utilize is a network pretrained on MS COCO followed by fine tuning/training on prepared PCB via images. Finally, we evaluate our results on two datasets, one PCB designed in house and another commercial PCB, and achieve peak results of 0.886, 0.936, 0.973, for intersection over union (IoU), Dice Coefficient, and Structural Similarity Index. These results vastly outperform our tuned implementation of Mask R-CNN.
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