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 this https URL.
Abstract (translated)
随着生成模型的进步，已经出现了高度逼真的人工智能（AI）生成的视频。恶意用户可以轻松地创建不存在的视频传播虚假信息。本文提出了一种通过捕获带两个分支时空卷积神经网络（CNN）的鉴定痕迹的有效人工智能生成视频（AIGVDet）方案。具体来说，分别学习两个ResNet子检测器来识别空间和光学流域中的异常。这样的子检测器的检测结果被融合以进一步增强识别能力。构建了一个大规模生成的视频数据集（GVD）作为模型训练和评估的基准。大量实验结果证实了我们AIGVDet方案的高通性和鲁棒性。代码和数据集将在这个链接处提供。
URL
https://arxiv.org/abs/2403.16638
