arXiv (All)
open access
AI or Real: Detecting Partially Altered Videos Under Resource-Constrained Environments
The proliferation of generative video models has shifted the practical detection threat from fully fabricated clips to partially manipulated footages. Although modern detectors achieve strong accuracy using foundation backbones of 400M+ parameters, their resource footprint precludes edge deployment.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Explicit Layer Modeling for Video Object Insertion and Video Layer Decomposition#1128626
- PIVOT: Physics-Grounded Verification for AI-Generated Audio-Video Detection#1109554
- Preserving Knowledge across Space and Time for Continual Video Deepfake Detection#926366
- LVMT: Video Mask Transformer for Long-term Video Segmentation#1131679
- SemanTok: Predictable Semantic Tokens for Efficient Autoregressive Video Generation#1155621
Record · ID 1034031
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive