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Shanmuk4622
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ai-image-detection-dataset

Tasks:
Image Classification
Modalities:
Image
Text
Languages:
English
Size:
100K - 1M
Tags:
ai-generated-image-detection
synthetic-image-detection
diffusion-models
image-forensics
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metadata 
license: other
task_categories:
  - image-classification
language:
  - en
tags:
  - ai-generated-image-detection
  - synthetic-image-detection
  - diffusion-models
  - image-forensics
pretty_name: AI-Image Detection Dataset
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path:
          - real/train/*.parquet
          - sd15/train/*.parquet
          - sdxl/train/*.parquet
          - flux_schnell/train/*.parquet
          - kandinsky22/train/*.parquet
          - pixart_sigma/train/*.parquet
          - wuerstchen/train/*.parquet
      - split: validation
        path:
          - real/val/*.parquet
          - sd15/val/*.parquet
          - sdxl/val/*.parquet
          - flux_schnell/val/*.parquet
          - kandinsky22/val/*.parquet
          - pixart_sigma/val/*.parquet
          - wuerstchen/val/*.parquet
      - split: test
        path:
          - real/test/*.parquet
          - sd15/test/*.parquet
          - sdxl/test/*.parquet
          - flux_schnell/test/*.parquet
          - kandinsky22/test/*.parquet
          - pixart_sigma/test/*.parquet
          - wuerstchen/test/*.parquet
  - config_name: real
    data_files:
      - split: train
        path: real/train/*.parquet
      - split: validation
        path: real/val/*.parquet
      - split: test
        path: real/test/*.parquet
  - config_name: sd15
    data_files:
      - split: train
        path: sd15/train/*.parquet
      - split: validation
        path: sd15/val/*.parquet
      - split: test
        path: sd15/test/*.parquet
  - config_name: sdxl
    data_files:
      - split: train
        path: sdxl/train/*.parquet
      - split: validation
        path: sdxl/val/*.parquet
      - split: test
        path: sdxl/test/*.parquet
  - config_name: flux_schnell
    data_files:
      - split: train
        path: flux_schnell/train/*.parquet
      - split: validation
        path: flux_schnell/val/*.parquet
      - split: test
        path: flux_schnell/test/*.parquet
  - config_name: kandinsky22
    data_files:
      - split: train
        path: kandinsky22/train/*.parquet
      - split: validation
        path: kandinsky22/val/*.parquet
      - split: test
        path: kandinsky22/test/*.parquet
  - config_name: pixart_sigma
    data_files:
      - split: train
        path: pixart_sigma/train/*.parquet
      - split: validation
        path: pixart_sigma/val/*.parquet
      - split: test
        path: pixart_sigma/test/*.parquet
  - config_name: wuerstchen
    data_files:
      - split: train
        path: wuerstchen/train/*.parquet
      - split: validation
        path: wuerstchen/val/*.parquet
      - split: test
        path: wuerstchen/test/*.parquet

AI-Image Detection Dataset

Paired real / AI images, with shared image-grounded captions, for training and evaluating AI-generated-image detectors.

Each of 10,000 real photos is captioned once (BLIP-2) and paired with one synthetic partner per generator (6 generators → 60,000 AI images). A real image and all of its AI partners share the same prompt, so the only systematic difference between the classes is the generative process itself. A detector trained here is pushed toward the synthesis fingerprint (texture, frequency, rendering artefacts) instead of scene content — the shortcut that inflates accuracy on naively-built datasets.

This is the clean, self-contained release. Every row is complete on its own: the image renders in the viewer, carries its prompt, its train/val/test split, and full caption + generation provenance — no need to join against side files. The viewer exposes a default subset (all 70k) plus one subset per model, each with its own train/validation/test splits.

At a glance

Real images 10,000 (COCO + ImageNet-1k), label 0
AI images 60,000 across 6 generators, label 1
Total rows 70,000
Pairs 10,000 (each real ↔ 6 AI partners, shared caption)
Resolution 512×512 RGB PNG
Splits train 49,392 / validation 10,122 / test 10,486 (pair-level, seed 42)
Subsets default + real, sd15, sdxl, flux_schnell, kandinsky22, pixart_sigma, wuerstchen
Preprocessing Identical canonical pipeline for real and AI (pipeline_version 1.2)
License Non-commercial research use (most-restrictive inherited term)

Subsets

The Dataset Viewer and load_dataset expose eight subsets, each split into train / validation / test:

subset rows contents
default 70,000 everything (real + all generators)
real 10,000 real photos only (label 0)
sd15 10,000 Stable Diffusion 1.5 outputs
sdxl 10,000 SDXL outputs
flux_schnell 10,000 FLUX.1-schnell outputs
kandinsky22 10,000 Kandinsky 2.2 outputs
pixart_sigma 10,000 PixArt-Σ outputs
wuerstchen 10,000 Würstchen outputs

What changed vs. the earlier release

  • image is now the Hugging Face Image type → thumbnails render in the Dataset Viewer.
  • prompt is populated on real rows (each real image's own BLIP-2 caption), so a pair literally shares one caption.
  • split is a column and the files are organised into <subset>/{train,val,test}/, so load_dataset(..., split=...) and the viewer's split tabs work.
  • Per-model subsets are selectable directly in the viewer / loader.
  • Caption provenance added: raw_caption, caption_n_tokens, caption_model, plus the original BLIP-2 caption shards under captions/.

Generators

key model id native res steps guidance
sd15 stable-diffusion-v1-5/stable-diffusion-v1-5 512 20 7.0
sdxl stabilityai/stable-diffusion-xl-base-1.0 1024 8 7.0
flux_schnell black-forest-labs/FLUX.1-schnell 1024 4 0.0
kandinsky22 kandinsky-community/kandinsky-2-2-decoder 768 25 4.0
pixart_sigma PixArt-alpha/PixArt-Sigma-XL-2-1024-MS 1024 20 4.5
wuerstchen warp-ai/wuerstchen 1024 [20, 10] [4.0, 0.0]

How the pairs are built

Real images are captioned with Salesforce/blip2-opt-2.7b, the caption is cleaned and capped to 75 CLIP tokens, and that exact caption is reused by all six generators and also stored on the real row. source_real_id links every AI image back to its real partner (source_real_id == image_id for real rows).

Canonical preprocess (leak-free)

Both real and AI images pass through the same pipeline:

  1. EXIF-transpose → convert to RGB → center-crop to square → Lanczos resize to 512.
  2. JPEG-equalise (quality 95, 4:4:4) → save as PNG (compress level 6, no EXIF/ICC).

Applying an identical transform to both classes removes the resolution, colour-space and JPEG-artefact shortcuts that would otherwise let a model cheat.

Splits

Deterministic pair-level split keyed on source_real_id (seed 42): a real image and all six of its AI partners always land in the same fold, so there is no scene leakage between train / validation / test.

split images
train 49,392
validation 10,122
test 10,486

Dataset structure 

ai-image-detection-dataset/
├── real/            {train,val,test}/*.parquet    # 10k real photos, label 0
├── sd15/            {train,val,test}/*.parquet    # 10k AI per generator, label 1
├── sdxl/            {train,val,test}/*.parquet
├── flux_schnell/    {train,val,test}/*.parquet
├── kandinsky22/     {train,val,test}/*.parquet
├── pixart_sigma/    {train,val,test}/*.parquet
├── wuerstchen/      {train,val,test}/*.parquet
├── captions/        captions-*.parquet            # original BLIP-2 caption shards (provenance)
├── metadata/
│   ├── manifest.parquet        # id, pairing key, label, generator, split (no image bytes)
│   ├── splits.parquet          # source_real_id -> split
│   ├── captions.parquet        # consolidated real-image captions + provenance
│   ├── config.json             # build configuration (seeds, models, params)
│   └── validation_report.json  # leak-audit / sniff-test results
├── ai_detect_common.py         # shared preprocess + schema library
└── README.md

Usage 

from datasets import load_dataset

# everything, by split
ds = load_dataset("Shanmuk4622/ai-image-detection-dataset")          # default subset
train, val, test = ds["train"], ds["validation"], ds["test"]

ex = train[0]
ex["image"]     # PIL.Image (decoded automatically, renders in the viewer)
ex["label"]     # 0 = real, 1 = AI
ex["prompt"]    # shared caption (now present on real rows too)
ex["generator"] # 'real' or one of the 6 generators
ex["split"]     # 'train' | 'validation' | 'test'

# a single model's subset (real + one generator are separate subsets)
flux_test = load_dataset("Shanmuk4622/ai-image-detection-dataset", "flux_schnell", split="test")
real_train = load_dataset("Shanmuk4622/ai-image-detection-dataset", "real", split="train")

Schema

column type description
image Image struct{bytes, path} canonical 512×512 RGB PNG
image_id string globally unique, e.g. real_000001 / sdxl_000001
source_real_id string pairing key; equals image_id for real rows
label int8 0 = real, 1 = AI
generator string real, sd15, sdxl, flux_schnell, kandinsky22, pixart_sigma, wuerstchen
source_dataset string coco, imagenet, or <generator>
split string train / validation / test
prompt string shared image-grounded caption (now populated for real rows)
raw_caption string uncleaned BLIP-2 output
caption_n_tokens int16 CLIP-token length of prompt
caption_model string captioner used (Salesforce/blip2-opt-2.7b)
width / height int16 always 512
orig_width / orig_height int32 provenance only — never use as a training feature
gen_model_id string HF model id (null for real)
gen_steps / gen_guidance / gen_native_res int16 / float32 / int16 generation params (null for real)
seed int64 hash(master_seed, generator, source_real_id) (null for real)
pipeline_version string 1.2
sha256 string hex digest of the PNG bytes
created_utc string ISO-8601 UTC timestamp

Leak audit

A "sniff test" trains a lightweight classifier on each generator vs. real using only low-level statistics and checks separability stays healthy (<0.85 healthy, 0.85–0.95 investigate, >0.95 leak):

generator score
sd15 0.910
sdxl 0.973
flux_schnell 0.917
kandinsky22 0.958
pixart_sigma 0.943
wuerstchen 0.843
overall 0.860

Full results in metadata/validation_report.json.

Provenance, license and intended use

  • ImageNet content is non-commercial research only (ILSVRC terms).
  • COCO images are Flickr-sourced (Creative Commons).
  • AI images are synthetic outputs of the models listed above, each under its own license.
  • This dataset inherits the most restrictive applicable term: non-commercial research use. Not legal advice.

Limitations and caveats

  • Core set is text-to-image only (no img2img / reference conditioning); detectors trained here target the text-to-image threat model.
  • Caption quality (BLIP-2 concise sentences) bounds prompt diversity.
  • FLUX.1-schnell and Würstchen run with CPU offload on T4; generation conditions are otherwise consistent with standard inference settings.
  • Per-model step counts were reduced for speed (SDXL 8 steps, SD 1.5 20 steps), which is representative of real-world fast inference.

Citation 

@misc{aiimage_detection_dataset,
  title  = {AI-Image Detection Dataset},
  author = {Shanmuk4622},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/datasets/Shanmuk4622/ai-image-detection-dataset}}
}