README.md

metadata

license: other
task_categories:
  - image-classification
tags:
  - ai-generated-image-detection
  - deepfake-detection
  - wildfake
dataset_info:
  features:
    - name: image_bytes
      dtype: binary
    - name: split
      dtype: string
    - name: group
      dtype: string
    - name: category
      dtype: string
    - name: source_zip
      dtype: string
    - name: source_path
      dtype: string
    - name: width
      dtype: int32
    - name: height
      dtype: int32
    - name: condition
      dtype: string
    - name: family
      dtype: string
    - name: order
      dtype: string
  splits:
    - name: train
      num_examples: 30000
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

WildFake-Sample

A 30,000-image sample of WildFake (Hao et al., AAAI 2025, arXiv:2402.11843; original dataset), covering generators and real-image sources outside DDA/SID — a held-out generalization slice, not a copy of the full ~3.6M-image dataset. All credit for the images goes to WildFake's original authors. Built for Buxt-Codes/AIGI-Detection (branch LoRC-PC) — see that repo's HANDOFF.md for the evaluation methodology and results.

Composition

• Fake (19,500): 750 × 26 generators — GANs (BigGAN, StyleGAN, StarGAN, DF-GAN, GALIP, GigaGAN), non-SD diffusion (ADM, DDPM, DDIM, Imagen, VQDM, DALL-E 2/3, Midjourney v4/v5), SD-family (SDXL, OriginalSD, ControlNet, LoRA, LyCORIS, 2x personalized), other (MAGE, VQGAN, VQVAE, MAE).
• Real (10,500): 1,750 × 6 sources — LAION-5B, ImageNet, LSUN-Church, FFHQ, AFHQ, CelebA-HQ.

Files

data/train-*.parquet — one row per image. manifest.csv/.json and transform_plan.csv/.json — the same metadata as plain CSV/JSON.

Columns: image_bytes (raw file bytes, undecoded — decode with Image.open(io.BytesIO(row["image_bytes"]))), split, group, category, source_zip/source_path, width/height, condition (one of 14 transform-battery conditions, assigned round-robin per group, stratified by resolution), family, order (always transform_first: condition applied, then a final standardizing JPEG q=96 pass).

Full build methodology (HTTP range-request sampling, transform-assignment algorithm, source code): see the GitHub repo linked above.