Chemistry-aware admission defeats many molecular graph backdoors, yet ChemBack demonstrates that chemically valid, target-aligned backdoors remain effective across benchmarks and defenses.
Robustness- inspired defense against backdoor attacks on graph neural networks
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CP-GBA distills a queryable repository of promptable subgraph triggers via graph prompt learning to achieve transferable backdoor attacks on GNNs with state-of-the-art success rates across paradigms and defenses.
citing papers explorer
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Rethinking Molecular Graph Backdoors under Chemistry-aware Admission
Chemistry-aware admission defeats many molecular graph backdoors, yet ChemBack demonstrates that chemically valid, target-aligned backdoors remain effective across benchmarks and defenses.
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Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers
CP-GBA distills a queryable repository of promptable subgraph triggers via graph prompt learning to achieve transferable backdoor attacks on GNNs with state-of-the-art success rates across paradigms and defenses.