Your One-Stop Solution for AI-Generated Video Detection

2026-01-16

AI Summary

Current research on AI-generated video detection is hindered by limited-scale datasets, outdated generative models, insufficient semantic diversity, and the absence of a systematic benchmark. To address these limitations, this work introduces AIGVDBench—the first large-scale, high-quality, and technologically representative benchmark, encompassing 31 state-of-the-art generative models and over 440,000 videos. The authors conduct more than 1,500 evaluations across 33 detectors spanning four major categories, employing multidimensional metrics and eight in-depth analyses. This comprehensive study reveals four novel findings, identifies critical performance bottlenecks and generalization patterns, and publicly releases all data and code to foster systematic progress in the field.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent advances in generative modeling can create remarkably realistic synthetic videos, making it increasingly difficult for humans to distinguish them from real ones and necessitating reliable detection methods. However, two key limitations hinder the development of this field. \textbf{From the dataset perspective}, existing datasets are often limited in scale and constructed using outdated or narrowly scoped generative models, making it difficult to capture the diversity and rapid evolution of modern generative techniques. Moreover, the dataset construction process frequently prioritizes quantity over quality, neglecting essential aspects such as semantic diversity, scenario coverage, and technological representativeness. \textbf{From the benchmark perspective}, current benchmarks largely remain at the stage of dataset creation, leaving many fundamental issues and in-depth analysis yet to be systematically explored. Addressing this gap, we propose AIGVDBench, a benchmark designed to be comprehensive and representative, covering \textbf{31} state-of-the-art generation models and over \textbf{440,000} videos. By executing more than \textbf{1,500} evaluations on \textbf{33} existing detectors belonging to four distinct categories. This work presents \textbf{8 in-depth analyses} from multiple perspectives and identifies \textbf{4 novel findings} that offer valuable insights for future research. We hope this work provides a solid foundation for advancing the field of AI-generated video detection. Our benchmark is open-sourced at https://github.com/LongMa-2025/AIGVDBench.

Problem

• AI-generated video detection
• dataset limitations
• benchmarking
• generative modeling
• synthetic video

Innovation

• AI-generated video detection
• benchmark
• generative models
• dataset diversity
• deepfake detection