AI or Real: Detecting Partially Altered Videos Under Resource-Constrained Environments

arXiv:2609.20263 · cs.CV, cs.DC · Submitted 2026-07-28 · Read on arXiv

cs.CV, cs.DC

Submitted: 2026-07-28

Updated: 2026-07-28

Code: https://github.com/TMElyralab/MuseV

Terminology

• AUC — 32× in this paper · explained in 2 episodes
• Benchmark — 11× in this paper · explained in 2 episodes
• Calibration — 11× in this paper · explained in 7 episodes
• Distillation — 11× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Consistency — 4× in this paper · explained in 3 episodes
• Edge Deployment — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Feature Extraction — 3× in this paper · explained in 2 episodes
• Knowledge Distillation — 3× in this paper · explained in 11 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Attention Diversity — 1× in this paper
• CVF International Conference on Computer Vision — 1× in this paper
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Training-free Detection of Generated Videos via Spatial-Temporal Likelihoods
• GenVidBench: A 6-Million Benchmark for AI-Generated Video Detection
• DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark
• Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• Mora: Enabling Generalist Video Generation via A Multi-Agent Framework

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models