GNNReveal: A Novel Graph Neural Network-based Attack Method for IC Logic Gate De-Camouflaging

Recent advancement in circuit extraction poses new threats to Integrated Circuits (ICs) Intellectual Property (IP) protection. Hardware obfuscation by IC logic gate camouflaging results in logic gates with unknown functionalities in an extracted netlist, protecting manufactured ICs from circuit extraction. Logic gate decamouflaging attacks have been proposed. Conventional attacks are not suitable for large-scale camouflaging due to infeasible computation cost. Existing Graph Neural Network(GNN)-based methods are efficient, but are less effective in differentiating between FanIn and FanOut structures which hampers their effectiveness. In this paper, we propose a novel GNN-based attack method, namely GNNReveal, for logic gate de-camouflaging. Our proposed GNNReveal performs separate FI/FO aggregations to generate unique node embeddings for logic gates with different functionalities, allowing for direct node classification for logic gate de-camouflaging. Our experiments show that our proposed GNNReveal achieved high de-camouflaging accuracy and significantly outperformed competing methods by at most 20%.