TRACE: Trajectory Representation and Consistency Estimation for AI-Generated Video Detection

arXiv:2609.25775 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Terminology

• Trace — 102× in this paper · explained in 2 episodes
• Consistency — 25× in this paper · explained in 3 episodes
• Generalization — 15× in this paper · explained in 13 episodes
• AUC — 14× in this paper · explained in 2 episodes
• Robustness — 14× in this paper · explained in 5 episodes
• Generative Model — 12× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 10× in this paper
• Flow Matching — 9× in this paper · explained in 8 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Generative Models — 8× in this paper · explained in 2 episodes
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Real-Centered Trajectory Optimization — 6× in this paper
• Transformer — 6× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Flow Matching Model — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Temporal consistency — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Average Precision — 1× in this paper
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• REVEAL: Reasoning-Enhanced Forensic Evidence Analysis for Explainable AI-Generated Image Detection
• ClueAegis: Heuristic-to-Reasoning Cognitive-skill Learning for Unified Evidence-based Synthetic Image Detection
• ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection
• Rethinking the Readout: Unlocking Video Backbones for AI-Generated Video Detection
• HunyuanVideo: A Systematic Framework For Large Video Generative Models
• SphereVideo: Prototype-anchored Hyperspherical Boundary for Continual AI-generated Video Detection
• Preserving Forgery Artifacts: AI-Generated Video Detection at Native Scale
• Flow Matching for Generative Modeling
• Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos
• RCDN: Real-Centered Detection Network for Robust Face Forgery Identification
• LATTE: Latent Trajectory Embedding for Diffusion-Generated Image Detection
• Wan: Open and Advanced Large-Scale Video Generative Models
• Explainable Synthetic Image Detection through Diffusion Timestep Ensembling
• Time Step Generating: A Universal Synthesized Deepfake Image Detector
• LRD-Net: A Lightweight Real-Centered Detection Network for Cross-Domain Face Forgery Detection
• Diffusion Noise Feature: Accurate and Fast Generated Image Detection
• Open-Sora: Democratizing Efficient Video Production for All

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