| --- |
| 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](https://arxiv.org/abs/2402.11843); |
| [original dataset](https://modelscope.cn/datasets/hy2628982280/WildFake)), |
| 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](https://github.com/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. |
|
|
