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Found 5 papers, 1 papers with code
Assessing Robustness via Score-Based Adversarial Image Generation
In this work, we introduce Score-Based Adversarial Generation (ScoreAG), a novel framework that leverages the advancements in score-based generative models to generate adversarial examples beyond $\ell_p$-norm constraints, so-called unrestricted adversarial examples, overcoming their limitations.
Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness
We perform the first adversarial robustness study into Graph Neural Networks (GNNs) that are provably more powerful than traditional Message Passing Neural Networks (MPNNs).
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
Including these contributions, we demonstrate that adversarial training is a state-of-the-art defense against adversarial structure perturbations.
Revisiting Robustness in Graph Machine Learning
Many works show that node-level predictions of Graph Neural Networks (GNNs) are unrobust to small, often termed adversarial, changes to the graph structure.
Training Differentially Private Graph Neural Networks with Random Walk Sampling
We propose to solve this issue by training graph neural networks on disjoint subgraphs of a given training graph.
