Reverse Engineering

Reverse engineering aims to reconstruct the design or functionality of a system from its observable behavior or output, encompassing diverse applications from deciphering software and hardware to analyzing complex materials. Current research focuses on applying machine learning, particularly deep learning models like transformers and autoencoders, and graph neural networks, to automate this process across various domains, including software analysis, material characterization, and the security of quantum machine learning models. These advancements improve efficiency and accuracy in tasks like identifying software components, reconstructing CAD models from images, and detecting malicious modifications in machine learning systems, impacting fields ranging from cybersecurity to manufacturing.