AI-Generated Video Detection
End-to-end pipeline for detecting AI-generated videos with 99%+ accuracy across Sora, Runway AI, Stable Video Diffusion, and NeRF-based systems. Published at CVPR 2024.
Overview
While deepfake detection research has focused heavily on images, AI-generated videos present a fundamentally different forensic challenge. Generators like Sora, Runway AI, and Stable Video Diffusion introduce traces that are qualitatively different from image generators — and existing image detectors fail to catch them.
Approach
We engineered an end-to-end detection pipeline that exploits temporal inconsistencies and generator-specific forensic artifacts unique to video generation. The system uses CNNs with temporal forensic analysis to identify AI-generated content across multiple generative architectures.
Dataset
We constructed and released a large-scale benchmark dataset of 8M+ frames spanning:
- Transformer-based generators (Sora)
- Diffusion-based generators (Runway AI, Stable Video Diffusion)
- NeRF-based systems
Hosted on Hugging Face with 400+ community downloads and adopted by external research groups for benchmarking.
Results
- 99%+ accuracy across 4 major generator families
- Robust detection after H.264 re-compression
- Few-shot learning enables detection of new generators with minimal examples
Impact
- Published at CVPR Workshops 2024
- Dataset adopted by external research groups
- 400+ Hugging Face downloads