SVAttack: Spatial-Viewpoint Transfer Attack on Graph Convolutional Skeleton Action Recognition

Summary

Researchers investigate why adversarial attacks on graph convolutional skeleton action recognition models transfer poorly across architectures. They propose SVAttack, which combines a spatial gradient damper that suppresses model-specific spatial biases with a viewpoint-based constraint that improves imperceptibility. Across 3 benchmark datasets, 9 models, 7 attack methods and 2 defense methods, the approach significantly improves transferability while keeping skeleton motions visually plausible.

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