Datasets:

JoeLeelyf
/

ViF-Bench

task_categories:
  - video-text-to-text
license: cc-by-4.0
language:
  - en
tags:
  - video-detection
  - ai-generated-content
  - explainable-ai
  - multimodal

ViF-CoT-4K Dataset

This repository hosts the ViF-CoT-4K dataset, a key component of the Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning paper. Skyra is a specialized multimodal large language model (MLLM) designed to identify human-perceivable visual artifacts in AI-generated videos, leveraging them as grounded evidence for both detection and explanation.

Paper: Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning
Project Page: https://joeleelyf.github.io/Skyra/
Code: https://github.com/JoeLeelyf/Skyra

Introduction

The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. Most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. ViF-CoT-4K addresses this by providing a specialized dataset to train multimodal large language models (MLLMs) to identify human-perceivable visual artifacts in AI-generated videos and leverage them as grounded evidence for both detection and explanation.

ViF-CoT-4K represents the first large-scale AI-generated video artifact dataset with fine-grained human annotations, supporting the development of models capable of spatio-temporal artifact perception, explanation capability, and detection accuracy.

Hierarchical Artifact Taxonomy

The dataset defines a comprehensive taxonomy to categorize AI generation errors, dividing them into Low-level Forgery (e.g., texture/color anomalies) and Violation of Laws (e.g., physical inconsistencies).

Taxonomy of Artifacts

Dataset: ViF-CoT-4K

ViF-CoT-4K is constructed to address the lack of detailed artifact annotations in existing datasets.

Scale: ~4,000 videos, including high-quality samples from Sora-2, Wan2.1, Kling, and more.
Annotation: Fine-grained labels including artifact type, textual explanation, timestamps, and bounding boxes.
Real-Fake Pairs: Generated videos are semantically aligned with real counterparts to prevent shortcut learning.

Usage

Requirements

SFT Stage: follow LlaMA-Factory for environment setup.
RL Stage: follow verl for environment setup.
Inference: follow Qwen-2.5-VL for quick start and vLLM for deployment.

Data Preparation

Training data: Download and prepare the ViF-CoT-4K dataset from here.
Evaluation data: Download evaluation datasets (e.g., ViF-Bench) from here. And modify the path to your local directory in test_index.json. The test_index.json file should contain the following format:

{
    "Real": [
        "path_to_parsed_frames_dir/Real/gdymHI9S6gM-0",
        ...
    ],
    "LTX-Video-13B-T": [
        "path_to_parsed_frames_dir/Fake/LTX-Video-13B-T/gdymHI9S6gM-0",
        ...
    ],
    ...
}

Supervised Fine-Tuning (SFT)

We use LLaMA-Factory for SFT. You can start training after setup the dataset config following the instructions in the LLaMA-Factory repository.

cd train/LLaMA-Factory
bash train.sh

Reinforcement Learning (RL)

We use verl for RL training with GRPO, with adapted reward design provided in train/verl/verl/utils/reward_score/ladm.py.

Evaluation

Evaluate scripts are provided in the eval/ directory. You can run the evaluation script as follows:

inference: Run inference to get model predictions and explanations, save the results in a JSON file.

cd eval
bash scripts/Skyra/inference.sh
# or
python inference.py \
    --index_json /path_to/test_index.json \
    --model_path /path_to/Skyra-SFT \
    --model_name Skyra-SFT \
    --save_dir results/Skyra

evaluation: Evaluate the model predictions against ground truth and compute metrics.

cd eval
bash scripts/Skyra/eval.sh
# or
python eval.py \
    --json_file_path results/Skyra/Skyra-SFT_predictions.json

License

The ViF-CoT-4K dataset and Skyra model weights are released under the CC BY 4.0 license. Users must adhere to the terms of source datasets (Kinetics-400, Panda-70M, HD-VILA-100M).

Citation

If you find Skyra or ViF-CoT-4K useful, please cite our paper:

@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}, 
}