A distribution-based adversarial attack generates quality-preserving adversarial motions for skeleton action recognition without noise perturbations, outperforming prior methods in success rate and naturalness on two datasets via a new human-aligned quality metric.
Deeprobust: A pytorch library for adversarial attacks and defenses
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A large-scale standardized benchmark of GNN attacks and defenses reveals that target node selection and attacked-model training process can completely distort measured attack effectiveness.
LR-GMP unifies graph prompting via a low-rank Graph Message Prompt paradigm to achieve better generalization than component-specific methods.
citing papers explorer
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Quality-Preserving Imperceptible Adversarial Attack on Skeleton-based Human Action Recognition
A distribution-based adversarial attack generates quality-preserving adversarial motions for skeleton action recognition without noise perturbations, outperforming prior methods in success rate and naturalness on two datasets via a new human-aligned quality metric.
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Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation
A large-scale standardized benchmark of GNN attacks and defenses reveals that target node selection and attacked-model training process can completely distort measured attack effectiveness.
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Unified Graph Prompt Learning via Low-Rank Graph Message Prompting
LR-GMP unifies graph prompting via a low-rank Graph Message Prompt paradigm to achieve better generalization than component-specific methods.