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Found 4 papers, 3 papers with code
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning
This is the first study of adversarial attacks on GVFL.
Salient Feature Extractor for Adversarial Defense on Deep Neural Networks
Motivated by the observation that adversarial examples are due to the non-robust feature learned from the original dataset by models, we propose the concepts of salient feature(SF) and trivial feature(TF).
Graphfool: Targeted Label Adversarial Attack on Graph Embedding
To the best of our knowledge, this is the first targeted label attack technique.
GraphAttacker: A General Multi-Task GraphAttack Framework
To address this problem, we propose GraphAttacker, a novel generic graph attack framework that can flexibly adjust the structures and the attack strategies according to the graph analysis tasks.
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