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arXiv · 2403.16638

AI-Generated Video Detection via Spatio-Temporal Anomaly Learning

Jianfa Bai·Man Lin·Gang Cao
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Abstract

The advancement of generation models has led to the emergence of highly realistic artificial intelligence (AI)-generated videos. Malicious users can easily create non-existent videos to spread false information. This letter proposes an effective AI-generated video detection (AIGVDet) scheme by capturing the forensic traces with a two-branch spatio-temporal convolutional neural network (CNN). Specifically, two ResNet sub-detectors are learned separately for identifying the anomalies in spatical and optical flow domains, respectively. Results of such sub-detectors are fused to further enhance the discrimination ability. A large-scale generated video dataset (GVD) is constructed as a benchmark for model training and evaluation. Extensive experimental results verify the high generalization and robustness of our AIGVDet scheme. Code and dataset will be available at https://github.com/multimediaFor/AIGVDet.

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Jianfa Bai, Man Lin, Gang Cao. 2024-03-25. AI-Generated Video Detection via Spatio-Temporal Anomaly Learning. https://arxiv.org/abs/2403.16638

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