Chameleon: On the Scene Diversity and Domain Variety of AI-Generated Videos Detection
Journal:
arXiv
Published Date:
Mar 9, 2025
Abstract
Artificial intelligence generated content (AIGC), known as DeepFakes, has
emerged as a growing concern because it is being utilized as a tool for
spreading disinformation. While much research exists on identifying
AI-generated text and images, research on detecting AI-generated videos is
limited. Existing datasets for AI-generated videos detection exhibit
limitations in terms of diversity, complexity, and realism. To address these
issues, this paper focuses on AI-generated videos detection and constructs a
diverse dataset named Chameleon. We generate videos through multiple generation
tools and various real video sources. At the same time, we preserve the videos'
real-world complexity, including scene switches and dynamic perspective
changes, and expand beyond face-centered detection to include human actions and
environment generation. Our work bridges the gap between AI-generated dataset
construction and real-world forensic needs, offering a valuable benchmark to
counteract the evolving threats of AI-generated content.
