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.

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Author Of this post: <a href="">Ulbert J. Botero</a>, <a href="">Ronald Wilson</a>, <a href="">Hangwei Lu</a>, <a href="">Mir Tanjidur Rahman</a>, <a href="">Mukhil A. Mallaiyan</a>, <a href="">Fatemeh Ganji</a>, <a href="">Navid Asadizanjani</a>, <a href="">Mark M. Tehranipoor</a>, <a href="">Damon L. Woodard</a>, <a href="">Domenic Forte</a>

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