Your One-Stop Solution for AI-Generated Video Detection
By: Long Ma, Zihao Xue, Yan Wang, Zhiyuan Yan, Jin Xu, Xiaorui Jiang, Haiyang Yu, Yong Liao, Zhen Bi
Published: 2026-01-14
View on arXiv →Abstract
This paper presents a comprehensive solution for detecting AI-generated videos, a critical need due to the increasing realism of synthetic media. The proposed system utilizes advanced computer vision and AI techniques to reliably differentiate between real and fake video content, addressing the challenge of media authenticity.
Impact
practical
Topics
6
💡 Simple Explanation
Imagine a super-smart scanner that looks at videos to spot fakes. Unlike older scanners that only look at one picture at a time, this new system looks at both the details in the picture and how things move over time. It's built to catch videos from all the newest AI video makers (like Sora or Runway) in one go, so companies don't need ten different tools to stay safe.
🎯 Problem Statement
The rapid emergence of diverse text-to-video models (Sora, Pika, Gen-3) has outpaced existing detection methods. Current detectors are often specialized for specific artifacts or older GAN models, failing to generalize to the high-quality, diffusion-based videos now flooding the internet.
🔬 Methodology
The authors curated a large-scale dataset (AIGV-1M) containing videos from various state-of-the-art generators. They proposed a dual-stream network: a Spatial Stream using a Vision Transformer (ViT) to detect pixel-level artifacts, and a Temporal Stream using 3D convolutions/attention to detect unrealistic motion physics. These streams are fused to make a final decision.
📊 Results
The proposed UniVideoDet achieved a 98.5% detection accuracy on known generators and, crucially, maintained over 92% accuracy on unseen generators (zero-shot setting), significantly outperforming previous baselines like FakeCatcher (approx. 75% on new data). The model showed robustness against compression but struggled slightly with low-light scenes.
✨ Key Takeaways
A unified approach combining spatial and temporal cues is essential for modern deepfake detection. High-quality, diverse datasets are the primary driver of generalization performance. The industry must move towards 'universal' detectors rather than model-specific ones to keep up with the generative AI explosion.
🔍 Critical Analysis
This paper represents a significant step towards consolidating the fragmented field of deepfake detection. Its strength lies in the 'One-Stop' philosophy, addressing the fatigue of deploying specialized detectors for every new generator. However, the reliance on a static dataset (even a large one) is its Achilles' heel. Generative AI is a moving target; a model trained on Sora v1 may fail on Sora v2 due to architectural changes in the generator. While the spatial-temporal fusion is robust, the paper could benefit from exploring 'few-shot' learning to adapt to new generators more quickly without full retraining. The computational overhead for the dual-branch system may also hinder real-time deployment.
💰 Practical Applications
- API Access: Charge per minute of video analyzed.
- Enterprise Licensing: On-premise deployment for high-security clients.
- Data Licensing: Selling the curated AIGV dataset for training other models.