AI-Generated Video Detection via Spatio-Temporal Anomaly Learning
Jianfa Bai Man Lin Gang Cao
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.