Presentation
Detrojan: Detecting Trojan Circuits Using a Purpose-Built Graph Neural Network with Community-Enhanced Classification
DescriptionGate-level Hardware Trojan (HT) detection using Graph Neural Networks (GNNs) often suffers from limited accuracy due to the reliance on structure-only features and fragmented node-level predictions. We propose a GNN-based framework, \papertitle, to overcome these challenges through three key innovations:
(1) functionality-aware feature engineering,
(2) an edge-aware GNN architecture with Jumping Knowledge and global context aggregation, and
(3) accurate Trojan circuit localization with community-aware classification refinement.
Evaluated on the 2025 ICCAD CAD Contest Hardware Trojan benchmarks, DeTrojan achieves a 16.7% improvement in circuit-level prediction accuracy and a 47.3% relative increase in gate-level F1-score over the state-of-the-art machine learning (ML)-based methods. Furthermore, incorporating our proposed features and refinement modules into the existing approaches yield up to 16.6% gain in accuracy and a 2× improvement in the F1-score, demonstrating the effectiveness and generality of our framework.
(1) functionality-aware feature engineering,
(2) an edge-aware GNN architecture with Jumping Knowledge and global context aggregation, and
(3) accurate Trojan circuit localization with community-aware classification refinement.
Evaluated on the 2025 ICCAD CAD Contest Hardware Trojan benchmarks, DeTrojan achieves a 16.7% improvement in circuit-level prediction accuracy and a 47.3% relative increase in gate-level F1-score over the state-of-the-art machine learning (ML)-based methods. Furthermore, incorporating our proposed features and refinement modules into the existing approaches yield up to 16.6% gain in accuracy and a 2× improvement in the F1-score, demonstrating the effectiveness and generality of our framework.
Event Type
Research Manuscript
TimeTuesday, July 2810:30am - 10:43am PDT
LocationMtg Room 203C