Datasetpublic

Shanmuk4622/ai-detection-dataset-v2

---dataset_info: features: - name: image # use the exact column name from your parquet schema dtype: image # this forces Hugging Face to render it as an image - name: label dtype: string license: other task_categories: - image-classification language: - en tags: - ai-generated-image-detection - synthetic-image-detection - diffusion-models pretty_name: AI-Generated Image Detection Dataset v2 size_categories: - 10K<n<100K AI-Generated… See the full description on the dataset page: https://huggingface.co/datasets/Shanmuk4622/ai-detection-dataset-v2.

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Dataset Card

license: other task_categories:

—image-classification language:
—en tags:
—ai-generated-image-detection
—synthetic-image-detection
—diffusion-models prettyname: AI-Generated Image Detection Dataset v2 sizecategories:
—10K<n<100K ---

AI-Generated Image Detection Dataset v2

Paired real / AI images for training and evaluating AI-generated image detectors. Every real image has one AI partner per generator, and the pair shares a single image-grounded caption — so detectors are pushed toward the synthesis fingerprint (texture, frequency, rendering artefacts) rather than scene content.

At a glance


Real images	10,000 (COCO + ImageNet-1k), label `0`
AI images	60,000 across 6 generators, label `1`
Pairs	10,000 (each real has 6 AI partners)
Resolution	512×512 RGB PNG
Preprocessing	Identical canonical pipeline for real and AI (`pipeline_version=1.2`)
Master seed	`42` — controls generation seeds and split assignment
Split	70/15/15 pair-level train/val/test

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]

Pairing and captions

Real images were captioned with Salesforce/blip2-opt-2.7b, cleaned and capped to 75 CLIP tokens, then reused byte-identically by all six generators. 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 go through the same pipeline:

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

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

Splits

Deterministic pair-level split keyed on source_real_id (seed 42): every real image and all six of its AI partners land in the same fold. The split map is in splits.parquet; the full index is in manifest.parquet.

Split	Images
train	49392
val	10122
test	10486

Loading

python

from huggingface_hub import hf_hub_download
import pyarrow.parquet as pq
from PIL import Image
import io

# Read the manifest (no image bytes — fast)
man = pq.read_table(hf_hub_download("Shanmuk4622/ai-detection-dataset-v2", "manifest.parquet", repo_type="dataset"))

# Read image bytes from a shard
shard = hf_hub_download("Shanmuk4622/ai-detection-dataset-v2", "real/real-xxxxx-00000.parquet", repo_type="dataset")
tbl   = pq.read_table(shard)
img   = Image.open(io.BytesIO(tbl["image"][0].as_py()))

Schema

column	type	description
`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` or `sd15` or `sdxl` or `flux_schnell` or `kandinsky22` or `pixart_sigma` or `wuerstchen`
`source_dataset`	string	`coco` or `imagenet` or `<generator>`
`prompt`	string	caption used (AI rows); null for real
`image`	binary	canonical 512×512 RGB PNG bytes
`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
`gen_steps` / `gen_guidance` / `gen_native_res`	int16/float32/int16	generation params
`seed`	int64	`hash(master_seed, generator, source_real_id)`
`caption_model`	string	captioner used
`pipeline_version`	string	`1.2`
`sha256`	string	hex digest of the PNG bytes
`created_utc`	string	ISO-8601 UTC timestamp

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.
—Intended for training / evaluating AI-image detectors. See validation_report.json for the leak-audit results.

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); this is representative of real-world fast inference.