Aggressive time-to-market constraints and enormous hardware design and
fabrication costs have pushed the semiconductor industry toward hardware
Intellectual Properties (IP) core design. However, the globalization of the
integrated circuits (IC) supply chain exposes IP providers to theft and illegal
redistribution of IPs. Watermarking and fingerprinting are proposed to detect
IP piracy. Nevertheless, they come with additional hardware overhead and cannot
guarantee IP security as advanced attacks are reported to remove the watermark,
forge, or bypass it. In this work, we propose a novel methodology, GNN4IP, to
assess similarities between circuits and detect IP piracy. We model the
hardware design as a graph and construct a graph neural network model to learn
its behavior using the comprehensive dataset of register transfer level codes
and gate-level netlists that we have gathered. GNN4IP detects IP piracy with
96% accuracy in our dataset and recognizes the original IP in its obfuscated
version with 100% accuracy.

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