1 code implementation • 25 Oct 2022 • Haibin Zheng, Haiyang Xiong, Jinyin Chen, Haonan Ma, Guohan Huang
Most of the proposed studies launch the backdoor attack using a trigger that either is the randomly generated subgraph (e. g., erd\H{o}s-r\'enyi backdoor) for less computational burden, or the gradient-based generative subgraph (e. g., graph trojaning attack) to enable a more effective attack.
1 code implementation • 14 Aug 2022 • Haibin Zheng, Haiyang Xiong, Haonan Ma, Guohan Huang, Jinyin Chen
Consequently, the link prediction model trained on the backdoored dataset will predict the link with trigger to the target state.
no code implementations • 26 Nov 2021 • Jinyin Chen, Haiyang Xiong, Dunjie Zhang, Zhenguang Liu, Jiajing Wu
Phishing detectors direct their efforts in hunting phishing addresses.
no code implementations • 8 Oct 2021 • Jinyin Chen, Haiyang Xiong, Haibin Zheng, Jian Zhang, Guodong Jiang, Yi Liu
Backdoor attacks induce the DLP methods to make wrong prediction by the malicious training data, i. e., generating a subgraph sequence as the trigger and embedding it to the training data.
1 code implementation • 16 Jul 2021 • Jinyin Chen, Haiyang Xiong, Haibin Zhenga, Dunjie Zhang, Jian Zhang, Mingwei Jia, Yi Liu
To achieve lower-complexity defense applied to graph classification models, EGC2 utilizes a centrality-based edge-importance index to compress the graphs, filtering out trivial structures and adversarial perturbations in the input graphs, thus improving the model's robustness.