README.md

metadata

license: cc-by-4.0
dataset_info:
  features:
    - name: image
      dtype: image
    - name: generator
      dtype: string
    - name: uid
      dtype: string
    - name: labels
      list:
        - name: label
          dtype: string
        - name: points
          list:
            list: float64
    - name: original_prompt
      dtype: string
    - name: positive_prompt
      dtype: string
    - name: negative_prompt
      dtype: string
    - name: guidance_scale
      dtype: float64
    - name: num_inference_steps
      dtype: int64
    - name: scheduler
      dtype: string
    - name: seed
      dtype: int64
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: image_format
      dtype: string
    - name: jpeg_quality
      dtype: int64
    - name: chroma_subsampling
      dtype: string
  splits:
    - name: labeled_train
      num_bytes: 1229331054
      num_examples: 918
    - name: labeled_test
      num_bytes: 3492466407
      num_examples: 2419
    - name: unlabeled_train
      num_bytes: 34599400559
      num_examples: 24013
    - name: unlabeled_test
      num_bytes: 35214906257
      num_examples: 24638
  download_size: 74508314134
  dataset_size: 74536104277
configs:
  - config_name: default
    data_files:
      - split: labeled_train
        path: data/labeled_train-*
      - split: labeled_test
        path: data/labeled_test-*
      - split: unlabeled_train
        path: data/unlabeled_train-*
      - split: unlabeled_test
        path: data/unlabeled_test-*
pretty_name: X-AIGD

X-AIGD

X-AIGD is a fine-grained benchmark designed for eXplainable AI-Generated image Detection. It provides pixel-level human annotations of perceptual artifacts in AI-generated images, spanning low-level distortions, high-level semantics, and cognitive-level counterfactuals, aiming to advance robust and explainable AI-generated image detection methods.

For more details, please refer to our paper: Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detection.

🎨 Artifact Taxonomy

We define a comprehensive artifact taxonomy comprising 3 levels and 7 specific categories to capture the diverse range of perceptual artifacts in AI-generated images.

• Low-level Distortions: low-level-edge_shape, low-level-texture, low-level-color, low-level-symbol.
• High-level Semantics: high-level-semantics.
• Cognitive-level Counterfactuals: cognitive-level-commonsense, cognitive-level-physics.

🚀 Dataset Contents

This repository currently hosts the pixel-level annotated subset of X-AIGD, which includes over 18,000 artifact instances across 3,000+ labeled samples, along with a large-scale unlabeled dataset.

Note on Dataset Status:

• labeled_train, labeled_test, unlabeled_train, and unlabeled_test splits are currently available.
• Real images are planned for upcoming release.

Data Fields

• image: The AI-generated image (PNG or JPEG format).
• generator: Name of the text-to-image generator.
• uid: Unique identifier for the image.
• labels: List of human-annotated artifacts, each containing:
  • label: Category of the artifact (e.g., low-level-edge_shape, high-level-semantics).
  • points: Polygon coordinates [[x1, y1], [x2, y2], ...] localizing the artifact.
• original_prompt, positive_prompt, negative_prompt: Text prompts used for generation.
• num_inference_steps, guidance_scale, seed, scheduler: Generation parameters.
• width, height: Image resolution.
• image_format, jpeg_quality, chroma_subsampling: Image compression details.

UID Correspondence

Each AI-generated (fake) image is generated based on the caption of a real image and inherits its uid from the corresponding real image metadata entry. This means the uid field in the fake splits matches the uid used across different generators, allowing direct pairing and comparison between images sharing the same semantic source.

📖 Usage Example

from datasets import load_dataset

# Load the labeled test split (AI-generated images with artifact annotations)
ds = load_dataset("Coxy7/X-AIGD", split="labeled_test")

# Access an example
sample = ds[0]
print(f"Generator: {sample['generator']}")
print(f"UID: {sample['uid']}")

# Access artifact labels and polygon localization
for artifact in sample["labels"]:
    print(f"Artifact category: {artifact['label']}")
    print(f"Polygon points: {artifact['points']}")

# The image is a PIL object
# sample["image"].show()

📝 Citation

If you find our work useful in your research, please consider citing:

@article{xiao2026unveiling,
  title={Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detection},
  author={Xiao, Yao and Chen, Weiyan and Chen, Jiahao and Cao, Zijie and Deng, Weijian and Yang, Binbin and Dong, Ziyi and Ji, Xiangyang and Ke, Wei and Wei, Pengxu and Lin, Liang},
  journal={arXiv preprint arXiv:2601.19430},
  year={2026}
}

📄 License

The dataset is released under the CC BY 4.0 license.