Disguising Topology and Side-Channel Information through Covert Gate- and ML-Enabled IP Camouflaging

• URL: http://arxiv.org/abs/2603.25904v1
• Date: Thu, 26 Mar 2026 20:53:10 GMT
• Title: Disguising Topology and Side-Channel Information through Covert Gate- and ML-Enabled IP Camouflaging
• Abstract summary: IP theft incurs hundreds of billions in annual losses, driven by advanced reverse engineering (RE) techniques.<n>Traditional cryptic'' IC camouflaging methods focus on hiding localized gate functionality but remain vulnerable to system-level structural analysis.<n>We show that a functional IP (F) is designed to structurally and visually masquerade as a completely different appearance IP (A)<n>Our results validate that this multi-layered approach effectively thwarts the entire RE toolchain by poisoning the structural and logical data used for netlist understanding.
• Score: 2.156373334386171
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semiconductor intellectual property (IP) theft incurs hundreds of billions in annual losses, driven by advanced reverse engineering (RE) techniques. Traditional ``cryptic'' IC camouflaging methods typically focus on hiding localized gate functionality but remain vulnerable to system-level structural analysis. This paper explores ``mimetic deception,'' where a functional IP (F) is designed to structurally and visually masquerade as a completely different appearance IP (A). We provide a comprehensive evaluation of three deceptive methodologies: IP Camouflage, Graph Matching, and DNAS-NAND Gate Array, analyzing their resilience against GNN-based node classification, and Differential Power Analysis (DPA). Crucially, we demonstrate that mimetic deception achieves a novel anti-side-channel defense: by forcing the mis-classification of cryptographic primitives, the adversary is led to apply an incorrect power model, causing the DPA attack to fail. Our results validate that this multi-layered approach effectively thwarts the entire RE toolchain by poisoning the structural and logical data used for netlist understanding.

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