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UID:dac_DAC 2026_sess315_RESEARCH2179@linklings.com
SUMMARY:DeTrojan: Detecting Trojan Circuits Using a Purpose-Built Graph Ne
 ural Network with Community-Enhanced Classification
DESCRIPTION:Chien-Tung (Cherie) Kuo, Chen-Ching Nieh, Jie-Hong Roland Jian
 g, and Chung-Yang (Ric) Huang (National Taiwan University)\n\nGate-level H
 ardware Trojan (HT) detection using Graph Neural Networks (GNNs) often suf
 fers 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: \n
 (1) functionality-aware feature engineering, \n(2) an edge-aware GNN archi
 tecture with Jumping Knowledge and global context aggregation, and\n(3) ac
 curate Trojan circuit localization with community-aware classification ref
 inement.\nEvaluated on the 2025 ICCAD CAD Contest Hardware Trojan benchmar
 ks, DeTrojan achieves a 16.7% improvement in circuit-level prediction accu
 racy and a 47.3% relative increase in gate-level F1-score over the state-o
 f-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, d
 emonstrating the effectiveness and generality of our framework.\n\nTopics:
  Quantum\n\nTrack: DES6. Quantum Computing, Systems\n\nSession Chairs: Hok
 eun Kim (Arizona State University); BaekGyu Kim (Daegu Gyeongbuk Institute
  of Science and Technology); Xiang Chen (Peking University); Sangyeob Kim 
 (Yonsei University); Robert M. Radway (University of Pennsylvania); Prasha
 nth Mohan (University of Pittsburgh); Xiaoxuan Yang (University of Virgini
 a, Stanford University); Ramtin Zand (University of South Carolina); Hadi 
 Kamali (University of Central Florida); Jiafeng Xie (Villanova University)
 ; Prabuddha Chakraborty (University of Maine); Junichiro Kadomoto (Univers
 ity of Tokyo); and Yosuke Ueno (RIKEN)\n\n
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