The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. However, most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. In this paper, we present Skyra, a specialized multimodal large language model (MLLM) that identifies human-perceivable visual artifacts in AI-generated videos and leverages them as grounded evidence for both detection and explanation. To support this objective, we construct ViF-CoT-4K for Supervised Fine-Tuning (SFT), which represents the first large-scale AI-generated video artifact dataset with fine-grained human annotations. We then develop a two-stage training strategy that systematically enhances our model's spatio-temporal artifact perception, explanation capability, and detection accuracy. Extensive experiments demonstrate that Skyra surpasses existing methods across multiple benchmarks, providing valuable insights for advancing explainable AI-generated video detection.
We propose a hierarchical taxonomy (L1: Low-level Forgery & Violation of Laws) containing categories like Physics Violation, Object Inconsistency, and Texture Anomaly to support fine-grained reasoning.
ViF-CoT-4K is the first large-scale dataset with manual fine-grained artifact annotations. It includes:
We employ a two-stage training pipeline:
Performance comparison on ViF-Bench across various generators.
Skyra achieves state-of-the-art performance on the proposed ViF-Bench and the external GenVideo benchmark.
| Method | Type | Acc (%) | F1 Score |
|---|---|---|---|
| DeMamba | Binary | 64.29 | 73.00 |
| GPT-4.1-mini | MLLM | 54.08 | 24.21 |
| Gemini-2.5-flash | MLLM | 53.36 | 57.48 |
| BusterX++ | MLLM-based | 56.90 | 21.94 |
| Skyra (Ours-RL) | MLLM-based | 91.02 | 90.27 |
[cite_start]Comparison on ViF-Bench (Mean). Skyra significantly outperforms both binary detectors and generic MLLMs. [cite: 378]
Skyra provides detailed textual explanations and spatial-temporal grounding (bounding boxes & timestamps) for its decisions.
@misc{li2025skyraaigeneratedvideodetection,
title={Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning},
author={Yifei Li and Wenzhao Zheng and Yanran Zhang and Runze Sun and Yu Zheng and Lei Chen and Jie Zhou and Jiwen Lu},
year={2025},
eprint={2512.15693},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2512.15693},
}