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AI Research
arXiv CS.AI·29d ago·5 min read
SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch-Level Incoherence and Temporal Roughness
TL;DR
Detecting AI-generated and partially edited videos is challenging due to their subtle alterations. SPLIT introduces a training-free method that identifies inconsistencies in spatial patches and temporal roughness.
✦ Why It Matters
Implement SPLIT in your video processing pipeline to enhance the detection of manipulated content in real-time applications.
Key Takeaways
- Utilize SPLIT for real-time detection of AI-generated videos.
- Leverage Two-step Temporal Roughness for analyzing video smoothness.
- Apply Local Spatial Motion Incoherence to identify spatial inconsistencies.
- Integrate SPLIT into existing video verification workflows for improved accuracy.
How It Works
SPLIT operates by analyzing video content through patch tokens from a frozen vision encoder. It computes Two-step Temporal Roughness (TTR) to evaluate the smoothness of patch movements and Local Spatial Motion Incoherence (LSMI) to detect spatial inconsistencies in motion.
These two metrics are combined multiplicatively with gamma correction to enhance the detection of fake videos, allowing for effective real-fake separation even at strict thresholds.