---
pretty_name: AIGI Inpainting Robustness
dataset_name: aigi-inpainting-robustness
tags:
  - ai-generated-images
  - inpainting
  - robustness
  - diffusion
  - watermarking
license: cc-by-nc-4.0
task_categories:
  - image-classification
  - other
size_categories:
  - 1K<n<10K
homepage: https://huggingface.co/datasets/eoguzakin/Robustness of AI-Generated Image Detection Against Localized Inpainting Attacks
---

# Robustness of AI-Generated Image Detection Against Localized Inpainting Attacks

This repository hosts the **detector-ready datasets** and **mask packs** used in the thesis:

> **Robustness of AI-Generated Image Detection Against Localized Inpainting Attacks**  
> *Oguz Akin, Saarland University, CISPA Helmholtz Center for Information Security (2025)*

It provides standardized evaluation splits for six state-of-the-art AI-generated image (AIGI) detectors across **watermarking, passive, and training-free paradigms**, tested under **LaMa** and **ZITS** inpainting attacks.  

Everything is packaged as `.tar.xz` archives to ensure reproducibility and easy transfer.

---

## 📂 Repository Structure

.
├─ detectors/
│ ├─ ufd_datasets.tar.xz
│ ├─ dimd_datasets.tar.xz
│ ├─ dire_datasets.tar.xz
│ ├─ aeroblade_datasets.tar.xz
│ ├─ stablesig_datasets.tar.xz
│ └─ treering_datasets.tar.xz
├─ masks/
│ ├─ masks_stablesig.tar.xz
│ └─ masks_treering_wm.tar.xz
└─ checksums.sha256


- **detectors/** — per-detector dataset “views,” already resized/re-encoded into the formats expected by each model.  
- **masks/** — random-rectangle and random-blob object masks (area-binned), used to generate inpainting attacks.  
- **checksums.sha256** — SHA-256 integrity hashes for all archives.

---

## 🔎 Dataset Details

### Detector Views

Each archive expands into the exact layout expected by that detector. All splits contain **200 images per split** (e.g. LaMa Inpainted Rand-Blob on SEMI-TRUTHS real images).

Typical layout:
baseline/
reals/
fakes/
[fakes_inpainted_lama/, fakes_inpainted_zits/]

robustness/
inpainted_lama/
randrect/
randblob_bins/bin{1..4}/
inpainted_zits/
randrect/
randblob_bins/bin{1..4}/
[reals_inpainted/]

---

### Detector Input Handling

**On disk:** All datasets are stored with their preprocessed versions for each detector to match their original paper/training setup.

- **UFD → 224**  
  (Resized + center-cropped to 224×224, CLIP normalization.)  
- **DIMD → JPEG-256**  
  (Resized to 256×256, with JPEG round-trip to mimic training distribution.)  
- **DIRE → 256**  
  (Resized to 256×256, matching the ADM ImageNet-256 diffusion prior.)  
- **AEROBLADE / StableSig / Tree-Ring → 512**  
  (All evaluated directly at 512×512 without JPEG compression.)

> **Why this split?** To eliminate the effect of compression or size on classification, ensuring scientifically fair evaluation.

---

### Mask Packs

- **masks_stablesig.tar.xz**  
- **masks_treering_wm.tar.xz**

Contain **random rectangle** and **random blob masks**, binned by area ratio:

- `bin1_0-3` → 0–3% of image area  
- `bin2_3-10` → 3–10%  
- `bin3_10-25` → 10–25%  
- `bin4_25-40` → 25–40%  

Used with **LaMa** and **ZITS** to create controlled inpainting attacks.

---

## 📐 Metrics

Datasets are organized to support a **fixed-threshold robustness evaluation**.

- **Baseline AUC**  
  Distinguish clean **reals vs fakes**. Threshold `t*` chosen via **Youden’s J**.  
- **Robustness AUC**  
  Distinguish **clean vs inpainted**.  
- **ΔAUC = Baseline – Robustness**  
- **ASR_inpainted** (primary):  
  % of **inpainted reals** classified as Real at baseline `t*`.  
- **ASR_fake→real** (secondary):  
  % of **baseline-detected fakes** that flip to Real after inpainting.  

**Watermarking detectors:**

- Thresholds fixed at **t90** and **t99** on clean watermarked images together with another threshold that is determined at baseline to reflect a real life setting.  
- **ASR** = % attacked watermarked images where watermark is not detected.  
- **AUC(clean vs attacked)** sanity check.

---

## 📦 Archive Sizes

- `detectors/aeroblade_datasets.tar.xz` — **1.5 GB**  
- `detectors/dimd_datasets.tar.xz` — **117 MB**  
- `detectors/dire_datasets.tar.xz` — **468 MB**  
- `detectors/stablesig_datasets.tar.xz` — **924 MB**  
- `detectors/treering_datasets.tar.xz` — **1.6 GB**  
- `detectors/ufd_datasets.tar.xz` — **442 MB**  
- `masks/masks_stablesig.tar.xz` — **2.2 MB**  
- `masks/masks_treering_wm.tar.xz` — **1.2 MB**

---

## ⚙️ Usage

### Download & Extract

```python
from huggingface_hub import hf_hub_download
import tarfile, os

REPO = "eoguzakin/Robustness of AI-Generated Image Detection Against Localized Inpainting Attacks"

def fetch_and_extract(filename, target_dir):
    path = hf_hub_download(repo_id=REPO, filename=filename, repo_type="dataset")
    os.makedirs(target_dir, exist_ok=True)
    with tarfile.open(path, "r:xz") as tar:
        tar.extractall(target_dir)
    print("Extracted:", target_dir)

# Example: UFD view + StableSig masks
fetch_and_extract("detectors/ufd_datasets.tar.xz", "/tmp/ufd")
fetch_and_extract("masks/masks_stablesig.tar.xz", "/tmp/masks_stablesig")
```

Integrity check
```bash
sha256sum -c checksums.sha256
```

🧪 Provenance
Reals: SEMI-TRUTHS (Pal et al. 2024), OpenImages subset.

Fakes: GenImage diverse generator set.

Inpainting attacks: LaMa (Suvorov et al. 2022), ZITS (Dong et al. 2022).

Watermarks: Stable Signature (Fernandez et al. 2023), Tree-Ring (Wen et al. 2023).

Detector-specific preprocessing applied before runtime, ensuring comparability.

---

## 📸 Sample Images

Baseline (Real vs Fake)


| Real | Fake |
|------|------|
| ![Real](samples/001a695ad732152b.jpg) | ![Fake](samples/ai_00003.png) |


Inpainted reals (LaMa, ZITS & SEMI-TRUTHS)

| LaMa | ZITS | SEMI-TRUTHS |
|------|------|-------------|
| ![LaMa](samples/001a695ad732152b_lama.png) | ![ZITS](samples/001a695ad732152b_zits.png) | ![SEMI-TRUTHS](samples/001a695ad732152b_m02wbtzl_9b6e60b8_semantic.png) |

| LaMa | ZITS |
|------|------|
| ![LaMa](samples/ai_00003_lama.png) | ![ZITS](samples/ai_00003_zits.png) |

Watermarks (StableSig vs Tree-Ring)

| StableSig | LaMa | ZITS |
|------|------|------|
| ![StableSig](samples/stsig_0007.png) | ![LaMa](samples/stsig_0007_lama.png) | ![ZITS](samples/stsig_0007_zits.png) |

| Tree-Ring | LaMa | ZITS |
|------|------|------|
| ![Tree-Ring](samples/0031_1671041020.png) | ![LaMa](samples/0031_1671041020_lama.png) | ![ZITS](samples/0031_1671041020_zits.png) |

---
📚 Citations
If you use this dataset, please cite: 
- Pal et al., 2024 — Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image Detectors. 
- Ojha et al., 2023 — Universal Fake Image Detectors. 
- Corvi et al., 2023 — On the Detection of Synthetic Images Generated by Diffusion Models. 
- Wang et al., 2023 — DIRE for Diffusion-Generated Image Detection. 
- Ricker et al., 2024 — AEROBLADE. Fernandez et al., 2023 — Stable Signature. 
- Wen et al., 2023 — Tree-Ring Watermarks. 
- Suvorov et al., 2022 — LaMa Inpainting. 
- Rombach et al., 2022 — Latent Diffusion Models. Dong et al., 2022 — ZITS Inpainting.

📝 License
Derived datasets for research use only.
Upstream datasets (SEMI-TRUTHS, GenImage, LaMa, ZITS, etc.) retain their original licenses.
This packaging (scripts + archive structure) is released under CC BY-NC 4.0 unless otherwise specified.

👤 Maintainer
Oguz Akin — Saarland University
Contact: ogak00001@stud.uni-saarland.de

🗓️ Changelog
v1.0 — Initial release with detector views + masks for LaMa and ZITS inpainting.