CyberIntel ⬡ News
★ Saved ◆ Cyber Reads
← Back ◬ AI & Machine Learning Mar 30, 2026

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

arXiv Security Archived Mar 30, 2026 ✓ Full text saved

arXiv:2603.25904v1 Announce Type: new 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 diff

Full text archived locally
✦ AI Summary · Claude Sonnet


    Computer Science > Cryptography and Security [Submitted on 26 Mar 2026] Disguising Topology and Side-Channel Information through Covert Gate- and ML-Enabled IP Camouflaging Junling Fan, David Koblah, Domenic Forte 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. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2603.25904 [cs.CR]   (or arXiv:2603.25904v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.25904 Focus to learn more Submission history From: Junling Fan [view email] [v1] Thu, 26 Mar 2026 20:53:10 UTC (648 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
    💬 Team Notes
    Article Info
    Source
    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Mar 30, 2026
    Archived
    Mar 30, 2026
    Full Text
    ✓ Saved locally
    Open Original ↗