Chameleon: On the Scene Diversity and Domain Variety of AI-Generated Videos Detection

• URL: http://arxiv.org/abs/2503.06624v1
• Date: Sun, 09 Mar 2025 13:58:43 GMT
• Title: Chameleon: On the Scene Diversity and Domain Variety of AI-Generated Videos Detection
• Authors: Meiyu Zeng, Xingming Liao, Canyu Chen, Nankai Lin, Zhuowei Wang, Chong Chen, Aimin Yang,
• Abstract summary: Existing datasets for AI-generated videos detection exhibit limitations in terms of diversity, complexity, and realism.<n>We generate videos through multiple generation tools and various real video sources.<n>At the same time, we preserve the videos' real-world complexity, including scene switches and dynamic perspective changes.
• Score: 4.66355848422886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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