Hu, Liang, Liu, Dora D., Zhang, Qi, Naseem, Usman, and Lai, Zhong Yuan (2023) Self-Supervised Learning for Multilevel Skeleton-Based Forgery Detection via Temporal-Causal Consistency of Actions. In: Proceedings of the AAAI Conference on Artificial Intelligence (37) pp. 844-853. From: AAAI-23: 37th AAAI Conference on Artificial Intelligence, 7-14 February 2023.

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Abstract

Skeleton-based human action recognition and analysis have become increasingly attainable in many areas, such as security surveillance and anomaly detection. Given the prevalence of skeleton-based applications, tampering attacks on human skeletal features have emerged very recently. In particular, checking the temporal inconsistency and/or incoherence (TII) in the skeletal sequence of human action is a principle of forgery detection. To this end, we propose an approach to self-supervised learning of the temporal causality behind human action, which can effectively check TII in skeletal sequences. Especially, we design a multilevel skeleton-based forgery detection framework to recognize the forgery on frame level, clip level, and action level in terms of learning the corresponding temporal-causal skeleton representations for each level. Specifically, a hierarchical graph convolution network architecture is designed to learn low-level skeleton representations based on physical skeleton connections and high-level action representations based on temporal-causal dependencies for specific actions. Extensive experiments consistently show state-of-the-art results on multilevel forgery detection tasks and superior performance of our framework compared to current competing methods.

Item ID:	79225
Item Type:	Conference Item (Research - E1)
ISSN:	2159-5399
Copyright Information:	Copyright © 2023, Association for the Advancement of Artificial Intelligence
Date Deposited:	13 Dec 2023 00:36
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 100%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100%
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