Explainability Methods for Hardware Trojan Detection: A Systematic Comparison

Hardware trojan detection requires accurate identification and interpretable explanations for security engineers to validate and act on results. This work compares three explainability categories for gate-level trojan detection on the Trust-Hub benchmark: (1) domain-aware property-based analysis of 31 circuit-specific features from gate fanin patterns, flip-flop distances, and I/O connectivity; (2) case-based reasoning using k-nearest neighbors for precedent-based explanations; and (3) model-agnostic feature attribution (LIME, SHAP, gradient).