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
this https URL

该研究探讨了有效的基于人工智能生成视频的检测方案，通过使用两个分支的时空卷积神经网络来捕捉取证迹象，对视频的空间和光流异常进行分析，通过融合子检测器的结果来提高判别能力，并构建了一个大规模生成的视频数据集作为模型训练和评估的基准。