Hardware Trust and Assurance through Reverse Engineering: A Survey and Outlook from Image Analysis and Machine Learning Perspectives

TL;DR

This paper surveys how image analysis and machine learning can improve reverse engineering of hardware components like ICs and PCBs, addressing security, IP, and trust issues.

Contribution

It provides a comprehensive overview of challenges and future directions in applying image processing and machine learning to hardware reverse engineering for trust and assurance.

Findings

Identifies key challenges in hardware reverse engineering.

Highlights the role of AI techniques in enhancing accuracy.

Proposes a roadmap for future research in hardware trust.

Abstract

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 paper 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 paper presents a roadmap indicating clearly the actions to be taken to fulfill hardware trust and assurance objectives.