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In this work, we propose the first backdoor attack to graph neural networks (GNN).
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Certified Adversarial Robustness with Additive Noise. In NeurIPS
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ABS: Scanning neural networks for back-doors by artificial brain stimulation. In SIGSAC
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Adversarial Attack on Graph Structured Data. In ICML
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Spectral signatures in backdoor attacks. In NeurIPS
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Adversarial attacks on neural networks for graph data. In KDD . 2847–2856
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Adversarial Attacks on Node Embeddings via Graph Poisoning. In ICML
Aleksandar Bojchevski and Stephan Günnemann. 2019 · 2019
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How powerful are graph neural networks?. In ICLR
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Adversarial attacks on graph neural networks via meta learning. In ICLR
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Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation. In AAAI
Alexander Levine and Soheil Feizi. 2020 · 2020
Closest in time.
Dynamic Backdoor Attacks Against Machine Learning Models
Ahmed Salem, Rui Wen, Michael Backes, Shiqing Ma, and Yang Zhang. 2020 · 2020
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On Certifying Robustness against Backdoor Attacks via Randomized Smoothing. In CVPR Workshop
Binghui Wang, Xiaoyu Cao, Jinyuan Jia, and Neil Zhenqiang Gong. 2020 · 2020
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RAB: Provable Robustness Against Backdoor Attacks
Maurice Weber, Xiaojun Xu, Bojan Karlas, Ce Zhang, and Bo Li. 2020 · 2020
Closest in time.