---
license: cc-by-nc-sa-4.0
pretty_name: CoVAtt-Benchmark
size_categories:
  - 100K<n<1M
task_categories:
  - image-classification
tags:
  - image-attribution
  - synthetic-image-detection
  - ai-generated-images
  - open-set-recognition
  - text-to-image
configs:
  - config_name: GLIDE
    data_files:
      - split: v1
        path: data/GLIDE/v1/*.tar
      - split: v2
        path: data/GLIDE/v2/*.tar
  - config_name: GALIP
    data_files:
      - split: v1
        path: data/GALIP/v1/*.tar
      - split: v2
        path: data/GALIP/v2/*.tar
  - config_name: LDM
    data_files:
      - split: v1
        path: data/LDM/v1/*.tar
      - split: v2
        path: data/LDM/v2/*.tar
  - config_name: SD1.4
    data_files:
      - split: v1
        path: data/SD1.4/v1/*.tar
      - split: v2
        path: data/SD1.4/v2/*.tar
  - config_name: SDXL
    data_files:
      - split: v1
        path: data/SDXL/v1/*.tar
      - split: v2
        path: data/SDXL/v2/*.tar
  - config_name: SDXL-Turbo
    data_files:
      - split: v1
        path: data/SDXL-Turbo/v1/*.tar
      - split: v2
        path: data/SDXL-Turbo/v2/*.tar
  - config_name: Cascade
    data_files:
      - split: v1
        path: data/Cascade/v1/*.tar
      - split: v2
        path: data/Cascade/v2/*.tar
  - config_name: Hyper-SD
    data_files:
      - split: v1
        path: data/Hyper-SD/v1/*.tar
      - split: v2
        path: data/Hyper-SD/v2/*.tar
  - config_name: SD3
    data_files:
      - split: v1
        path: data/SD3/v1/*.tar
      - split: v2
        path: data/SD3/v2/*.tar
  - config_name: SD3.5
    data_files:
      - split: v1
        path: data/SD3.5/v1/*.tar
      - split: v2
        path: data/SD3.5/v2/*.tar
  - config_name: SD3.5-Turbo
    data_files:
      - split: v1
        path: data/SD3.5-Turbo/v1/*.tar
      - split: v2
        path: data/SD3.5-Turbo/v2/*.tar
  - config_name: FLUX
    data_files:
      - split: v1
        path: data/FLUX/v1/*.tar
      - split: v2
        path: data/FLUX/v2/*.tar
  - config_name: DALL-E
    data_files:
      - split: v1
        path: data/DALL-E/v1/*.tar
      - split: v2
        path: data/DALL-E/v2/*.tar
---

# CoVAtt-Benchmark

A large-scale benchmark for **generated-image attribution**: given an image
that was produced by some text-to-image model, decide **which** model
produced it, and decide whether it came from a model the system has never
seen before.

## What this dataset is

This is the dataset used to train and evaluate **CoVAtt**
(*Content-Based Verification for Attribution of AI-Generated Images*,
BMVC 2026). CoVAtt is a Siamese network that takes a **pair** of images and
predicts whether they came from the same generator. That single pairwise
decision is then used two ways:

- **Closed-set attribution** — match a query image against reference images
  from each known generator and attribute it to the best-matching one.
- **Open-set detection** — flag a query image whose similarity to *every*
  known generator is too low, i.e. it came from a generator outside the
  known set.

The dataset itself is generator-agnostic, so it is equally usable for plain
closed-set classification, synthetic-image detection, or any other
attribution method.

**Contents:** 254,893 generated images from **13 text-to-image generators**,
spanning the field from 2021 to 2024 — early GAN/diffusion systems (GALIP,
GLIDE, LDM), the Stable Diffusion line (SD1.4 through SD3.5), modern
high-quality models (FLUX, Stable Cascade), and distilled few-step variants
(SDXL-Turbo, SD3.5-Turbo, Hyper-SD). Total size is about 245 GB, packaged as
WebDataset-style `.tar` shards of roughly 1 GB each.

### Two design properties that make it an attribution benchmark

**1. Content is held constant across generators.** Every generator was
prompted with the *same* fixed list of COCO captions. So for any given
caption, there is a corresponding image from each of the 13 generators
depicting the same described scene. The systematic difference between two
generators' folders is therefore the generator itself, not the subject
matter — which is what forces a model to key on generator fingerprint rather
than on image content.

**2. Every generator was run twice (`v1` and `v2`).** The two batches cover
the *same* captions but are independent generation runs, so a caption's `v1`
and `v2` images depict the same scene while differing in sampling noise.
This yields same-generator image pairs that share no pixels, which is what
lets a pairwise model learn "same generator" as distinct from "same prompt".

## Quick start

Each generator is exposed as its own config, with `v1` and `v2` as splits.
The dataset is large, so streaming is recommended:

```python
from datasets import load_dataset

# One generator, one batch
ds = load_dataset("kenyag/CoVAtt-Benchmark", "SDXL", split="v1", streaming=True)
sample = next(iter(ds))
sample["png"]        # PIL image
sample["__key__"]    # sample_id, joins to metadata/samples.parquet
```

To recover the caption behind an image, or to work across generators, join on
`sample_id` using the manifest:

```python
import pandas as pd
from huggingface_hub import hf_hub_download

manifest = pd.read_parquet(hf_hub_download(
    "kenyag/CoVAtt-Benchmark", "metadata/samples.parquet", repo_type="dataset"))
manifest.head()   # sample_id, generator, batch, shard_file, member_name, caption, ...
```

## Layout

```
CoVAtt-Benchmark/
│
├── README.md
├── LICENSE
├── CITATION.cff
│
├── metadata/
│   ├── samples.parquet           # per-image manifest: sample_id, generator,
│   │                               batch, shard_file, member_name, caption,
│   │                               original_filename, file_size_bytes
│   ├── captions.csv              # master caption list used as the
│   │                               generation prompts, with COCO filenames
│   ├── generators.csv            # per-generator checkpoint, LoRA/base info,
│   │                               license, per-batch image counts
│   ├── generation_settings.yaml  # default-settings note
│   └── licenses/                 # <Generator>.txt: that checkpoint's
│                                   license + what it says about outputs
│
└── data/
    ├── GLIDE/{v1,v2}/part-NNN.tar
    ├── GALIP/{v1,v2}/part-NNN.tar
    ├── LDM/{v1,v2}/part-NNN.tar
    ├── SD1.4/{v1,v2}/part-NNN.tar
    ├── SDXL/{v1,v2}/part-NNN.tar
    ├── SDXL-Turbo/{v1,v2}/part-NNN.tar
    ├── Cascade/{v1,v2}/part-NNN.tar
    ├── Hyper-SD/{v1,v2}/part-NNN.tar
    ├── SD3/{v1,v2}/part-NNN.tar
    ├── SD3.5/{v1,v2}/part-NNN.tar
    ├── SD3.5-Turbo/{v1,v2}/part-NNN.tar
    ├── FLUX/{v1,v2}/part-NNN.tar
    └── DALL-E/{v1,v2}/part-NNN.tar
```

Archive members are named by a zero-padded numeric `sample_id`, not by the
caption text, because a few captions contain characters that are illegal in
paths. The original caption and filename are preserved in
`metadata/samples.parquet`.

Exact per-generator, per-batch image counts are in `metadata/generators.csv`.
They are near-identical across generators but not exactly equal: the two
generators run outside HuggingFace `diffusers` (GLIDE and GALIP) resolved a
slightly larger portion of the caption list than the `diffusers`-based ones.

## Generators and checkpoints

All generators were run with the **default pipeline settings** of the listed
checkpoint (no custom guidance scale, step count, or scheduler overrides),
except where noted. Full detail in `metadata/generators.csv`.

| `data/` folder | Model checkpoint | Checkpoint license | Notes |
|---|---|---|---|
| `GLIDE` | GLIDE (filtered): `base.pt` + `upsample.pt`, Dec 2021 release, via `openai/glide-text2im` | MIT | Only GLIDE checkpoint OpenAI ever released |
| `GALIP` | GALIP, COCO-pretrained (`pre_coco.pth`), via `tobran/GALIP` | Academic research use only (see note below) | GAN, not a diffusion model |
| `LDM` | `CompVis/ldm-text2im-large-256` | Apache-2.0 | |
| `SD1.4` | `CompVis/stable-diffusion-v1-4` | CreativeML OpenRAIL-M | |
| `SDXL` | `stabilityai/stable-diffusion-xl-base-1.0` (+ `stabilityai/stable-diffusion-xl-refiner-1.0`) | CreativeML Open RAIL++-M | Base + refiner |
| `SDXL-Turbo` | `stabilityai/sdxl-turbo` | Stability AI Community License Agreement | Distilled, few-step |
| `Cascade` | `stabilityai/stable-cascade-prior` + `stabilityai/stable-cascade` | Stability AI Non-Commercial Research Community License | |
| `Hyper-SD` | `ByteDance/Hyper-SD` LoRA (`Hyper-SDXL-1step-lora.safetensors`) on `stabilityai/stable-diffusion-xl-base-1.0` | ByteDance Hyper-SD License | 1-step distilled LoRA |
| `SD3` | `stabilityai/stable-diffusion-3-medium-diffusers` | Stability AI Community License Agreement | |
| `SD3.5` | `stabilityai/stable-diffusion-3.5-large` | Stability AI Community License Agreement | |
| `SD3.5-Turbo` | `stabilityai/stable-diffusion-3.5-large-turbo` | Stability AI Community License Agreement | Distilled, few-step |
| `FLUX` | `black-forest-labs/FLUX.1-dev` | FLUX.1 [dev] Non-Commercial License | |
| `DALL-E` | `fluently/Fluently-XL-v2` + LoRA `ehristoforu/dalle-3-xl-v2` | CreativeML OpenRAIL-M (LoRA) + Fluently Models License (base) | DALL-E-3-*style* LoRA on an SDXL finetune, **not** the OpenAI DALL-E 3 API — named to avoid misattribution |

Full per-generator license text, and what each license says about generated
outputs specifically, is in `metadata/licenses/<Generator>.txt` and
`metadata/generators.csv`.

## Real images

The captions come from **COCO train2017** (https://cocodataset.org). The real
COCO images themselves are **not redistributed here** — if your setup needs
real images alongside the generated ones, download them from COCO directly
under its own license and terms. `metadata/captions.csv` records the COCO
filename each caption came from, so generated images can be aligned back to
their real counterparts.

## Limitations and intended use

- Captions come from a single source domain (COCO: everyday scenes, objects,
  people). Attribution performance measured here may not transfer unchanged
  to other prompt distributions such as portraits, art styles, or text-heavy
  images.
- All images are stored as originally generated, without post-processing.
  Robustness to JPEG recompression, resizing, or social-media pipelines is
  **not** represented in this data and has to be simulated separately.
- Generators were run at their default settings. Changing sampler, step
  count, or guidance scale can shift a generator's fingerprint, so a model
  trained only on this data may degrade on non-default sampling.
- This dataset is intended for research on attribution, provenance, and
  synthetic-image detection. It is not intended for training generative
  models.

## License

Two layers apply:

- **The dataset compilation** (folder/shard structure,
  `metadata/samples.parquet`, `captions.csv`, `generators.csv`,
  `generation_settings.yaml`, README, citation) is released under
  **CC BY-NC-SA 4.0**. See `LICENSE`.
- **Each generated image** additionally carries the license of the checkpoint
  that produced it — see the *Checkpoint license* column above and
  `metadata/licenses/<Generator>.txt` for the full text and what that license
  says about generated outputs specifically. Most of these checkpoints
  disclaim any ownership of or restriction on their outputs, even where the
  model weights themselves are non-commercial (FLUX.1-dev, Stable Cascade,
  SDXL-Turbo). **GALIP is the exception**: its license is
  academic-research-only, and that restriction is treated as applying to its
  images in this release too.

## Citation

```bibtex
@inproceedings{Yaggel_2026_BMVC,
  author    = {Ken Yaggel and Edita Grolman and Hiroo Saito and Yuto Yamaji and Misaki Komatsu and Yoshikazu Hanatani and Asaf Shabtai and Yuval Elovici},
  title     = {CoVAtt - Content-Based Verification for Attribution of AI-Generated Images},
  booktitle = {British Machine Vision Conference 2026, {BMVC} 2026},
  publisher = {BMVA},
  year      = {2026},
  note      = {Accepted; proceedings forthcoming}
}
```

See also `CITATION.cff`.