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[2601.18696] Explainability Methods for Hardware Trojan Detection: A Systematic Comparison
Summary
This article systematically compares various explainability methods for detecting hardware trojans, focusing on their effectiveness in providing actionable insights for hardware engineers.
Why It Matters
As hardware trojans pose significant security risks to integrated circuits, understanding and improving detection methods is crucial. This study evaluates different explainability techniques to enhance the reliability of hardware security applications, which is vital for safeguarding technological infrastructure.
Key Takeaways
- Hardware trojans are malicious circuits that compromise IC security.
- Existing detection methods often yield high false positive and negative rates.
- The study compares domain-aware analysis, case-based reasoning, and feature attribution methods for better explainability.
- Improved explainability can lead to more reliable hardware security solutions.
- The findings are relevant for hardware engineers and security professionals.
Computer Science > Machine Learning arXiv:2601.18696 (cs) [Submitted on 26 Jan 2026 (v1), last revised 22 Feb 2026 (this version, v3)] Title:Explainability Methods for Hardware Trojan Detection: A Systematic Comparison Authors:Paul Whitten, Francis Wolff, Chris Papachristou View a PDF of the paper titled Explainability Methods for Hardware Trojan Detection: A Systematic Comparison, by Paul Whitten and 2 other authors View PDF HTML (experimental) Abstract:Hardware trojans are malicious circuits which compromise the functionality and security of an integrated circuit (IC). These circuits are manufactured directly into the silicon and cannot be fixed by security patches like software. The solution would require a costly product recall by replacing the IC and hence, early detection in the design process is essential. Hardware detection at best provides statistically based solutions with many false positives and false negatives. These detection methods require more thorough explainable analysis to filter out false indicators. Existing explainability methods developed for general domains like image classification may not provide the actionable insights that hardware engineers need. A question remains: How do domain-aware property analysis, model-agnostic case-based reasoning, and model-agnostic feature attribution techniques compare for hardware security applications? This work compares three categories of explainability for gate-level hardware trojan detection on the Trust-Hub ...
