GenVidBench: A Challenging Benchmark for Detecting AI-Generated Video

• URL: http://arxiv.org/abs/2501.11340v1
• Date: Mon, 20 Jan 2025 08:58:56 GMT
• Title: GenVidBench: A Challenging Benchmark for Detecting AI-Generated Video
• Authors: Zhenliang Ni, Qiangyu Yan, Mouxiao Huang, Tianning Yuan, Yehui Tang, Hailin Hu, Xinghao Chen, Yunhe Wang,
• Abstract summary: We introduce GenVidBench, a challenging AI-generated video detection dataset with several key advantages. The dataset includes videos from 8 state-of-the-art AI video generators. It is analyzed from multiple dimensions and classified into various semantic categories based on their content.
• Score: 35.05198100139731
• License:
• Abstract: The rapid advancement of video generation models has made it increasingly challenging to distinguish AI-generated videos from real ones. This issue underscores the urgent need for effective AI-generated video detectors to prevent the dissemination of false information through such videos. However, the development of high-performance generative video detectors is currently impeded by the lack of large-scale, high-quality datasets specifically designed for generative video detection. To this end, we introduce GenVidBench, a challenging AI-generated video detection dataset with several key advantages: 1) Cross Source and Cross Generator: The cross-generation source mitigates the interference of video content on the detection. The cross-generator ensures diversity in video attributes between the training and test sets, preventing them from being overly similar. 2) State-of-the-Art Video Generators: The dataset includes videos from 8 state-of-the-art AI video generators, ensuring that it covers the latest advancements in the field of video generation. 3) Rich Semantics: The videos in GenVidBench are analyzed from multiple dimensions and classified into various semantic categories based on their content. This classification ensures that the dataset is not only large but also diverse, aiding in the development of more generalized and effective detection models. We conduct a comprehensive evaluation of different advanced video generators and present a challenging setting. Additionally, we present rich experimental results including advanced video classification models as baselines. With the GenVidBench, researchers can efficiently develop and evaluate AI-generated video detection models. Datasets and code are available at https://genvidbench.github.io.

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