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    This study introduces the first adversarial attack, CIASA, for skeleton-based action recognition using graph convolutional networks (GCNs). The attack generates realistic, yet deceptive, skeleton sequences that fool state-of-the-art models, highlighting vulnerabilities in deep learning.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep learning, particularly graph convolutional networks (GCNs), excels at skeleton-based human action recognition.
    • The robustness of these GCN models against adversarial attacks is largely uninvestigated due to the complexity of spatiotemporal skeleton data.

    Purpose of the Study:

    • To present the first adversarial attack specifically designed for GCN-based skeleton action recognition.
    • To evaluate the effectiveness and transferability of this novel attack method.

    Main Methods:

    • Introduction of the Constrained Iterative Attack for Skeleton Actions (CIASA), a targeted attack method.
    • CIASA perturbs joint locations while maintaining temporal coherence, spatial integrity, and anthropomorphic plausibility using physical constraints, spatial realignments, and generative network regularization.
    • Exploration of localized, semantically imperceptible attacks and their transferability in black-box scenarios.

    Main Results:

    • CIASA successfully fools state-of-the-art skeleton action recognition models with high confidence.
    • Perturbations demonstrate significant transferability across different models in black-box settings.
    • Adversarial skeleton sequences can induce deceptive actions in computer-generated RGB videos.

    Conclusions:

    • CIASA is an effective attack against graph-based skeleton action recognition.
    • The findings reveal a significant and imminent threat to spatiotemporal deep learning models.
    • This work underscores the need for developing robust defense mechanisms for action recognition systems.