--- license: mit task_categories: - image-classification language: - en tags: - ai-generated-image-detection - deepfake-detection - synthetic-image-detection - computer-vision - binary-classification - gan-detection - diffusion-model-detection size_categories: - 100K [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Dataset Size](https://img.shields.io/badge/Images-142.9K-blue.svg)]() [![Task](https://img.shields.io/badge/Task-Binary%20Classification-green.svg)]() **A comprehensive dataset for training AI-generated image detection models** [TIGAS Model](https://huggingface.co/H1merka/TIGAS) • [GitHub Repository](https://github.com/H1merka/TIGAS) ## Dataset Description The TIGAS Dataset is a large-scale collection of real and AI-generated images designed for training and evaluating AI-generated image detection models. It contains **142,902 images** from diverse sources, including state-of-the-art generative models. ### Key Features - **Binary classification task**: Real (label=0) vs AI-Generated/Fake (label=1) - **Diverse generators**: 19 different image sources including GANs and diffusion models - **Balanced split**: ~54% real, ~46% fake images - **High-quality annotations**: CSV format with image paths and labels - **Ready-to-use**: Compatible with PyTorch and standard ML pipelines ## Dataset Statistics ### Overall Distribution | Split | Total Images | Real (label=0) | Fake (label=1) | Real % | |-------|--------------|----------------|----------------|--------| | **Train** | 128,776 | 69,772 | 59,004 | 54.2% | | **Test** | 14,126 | 7,037 | 7,089 | 49.8% | | **Total** | 142,902 | 76,809 | 66,093 | 53.7% | ### Image Sources (Train Split) The dataset includes images from the following generators and sources: | Source | Images | Type | Description | |--------|--------|------|-------------| | `art002_4` | 10,986 | Mixed | Artistic images subset 4 | | `art002_1` | 10,801 | Mixed | Artistic images subset 1 | | `VQDM` | 9,518 | Generated | Vector Quantized Diffusion Model | | `sd14` | 9,517 | Generated | Stable Diffusion 1.4 | | `Midjourney` | 9,516 | Generated | Midjourney AI | | `Glide` | 9,513 | Generated | OpenAI GLIDE | | `wuk` | 9,510 | Mixed | Mixed source images | | `art002_3` | 8,295 | Mixed | Artistic images subset 3 | | `gaugan` | 7,992 | Generated | NVIDIA GauGAN | | `art002_2` | 6,911 | Mixed | Artistic images subset 2 | | `sd15_1` | 6,353 | Generated | Stable Diffusion 1.5 subset 1 | | `sd15_2` | 6,349 | Generated | Stable Diffusion 1.5 subset 2 | | `art001` | 5,966 | Mixed | Artistic images | | `ADM` | 4,756 | Mixed | Ablated Diffusion Model (ImageNet) | | `biggan` | 3,200 | Generated | BigGAN | | `stargan` | 3,198 | Generated | StarGAN (face manipulation) | | `sd_xl` | 3,196 | Generated | Stable Diffusion XL | | `face` | 1,600 | Mixed | Face images | | `DALLE2` | — | Generated | DALL-E 2 (fake only in subset) | ### Image Formats | Format | Count | Percentage | |--------|-------|------------| | PNG | 48,130 | 37.4% | | JPG | 44,414 | 34.5% | | JPEG | 34,632 | 26.9% | | jpeg | 1,600 | 1.2% | ## Dataset Structure ``` TIGAS/ ├── LICENSE # MIT License ├── README.md # This file ├── train/ │ ├── annotations01.csv # Training annotations (128,776 entries) │ └── images/ │ ├── ADM/ │ │ ├── 0_real/ # Real images from ImageNet │ │ └── 1_fake/ # Generated by ADM │ ├── art001/ │ │ ├── 0_real/ │ │ └── 1_fake/ │ ├── art002_1/ ... art002_4/ │ ├── biggan/ │ ├── DALLE2/ │ ├── face/ │ ├── gaugan/ │ ├── Glide/ │ ├── Midjourney/ │ ├── sd_xl/ │ ├── sd14/ │ ├── sd15_1/ │ ├── sd15_2/ │ ├── stargan/ │ ├── VQDM/ │ └── wuk/ └── test/ └── annotations01.csv # Test annotations (14,126 entries) ``` ### Annotation Format The CSV files contain two columns: ```csv image_path,label images\ADM\0_real\ILSVRC2012_val_00000005.JPEG,0 images\Midjourney\1_fake\image_001.png,1 ``` - **image_path**: Relative path to the image file (Windows-style backslashes) - **label**: Binary label where: - `0` = Real/Natural image - `1` = AI-Generated/Fake image > **Note**: The `test` split uses the same `images/` directory as `train` but with different image subsets defined in its annotation file. ## Usage ### Loading with Python ```python import pandas as pd from pathlib import Path from PIL import Image # Load annotations data_root = Path("TIGAS") train_df = pd.read_csv(data_root / "train" / "annotations01.csv") test_df = pd.read_csv(data_root / "test" / "annotations01.csv") # Convert Windows paths to current OS format train_df['image_path'] = train_df['image_path'].str.replace('\\', '/') # Load an image def load_image(row): img_path = data_root / "train" / row['image_path'] image = Image.open(img_path).convert('RGB') label = row['label'] return image, label # Example image, label = load_image(train_df.iloc[0]) print(f"Label: {'Real' if label == 0 else 'Fake'}") ``` ### Loading with PyTorch ```python import torch from torch.utils.data import Dataset, DataLoader from torchvision import transforms import pandas as pd from PIL import Image from pathlib import Path class TIGASDataset(Dataset): def __init__(self, root_dir, split='train', transform=None): self.root_dir = Path(root_dir) self.split = split self.transform = transform # Load annotations ann_path = self.root_dir / split / "annotations01.csv" self.annotations = pd.read_csv(ann_path) self.annotations['image_path'] = self.annotations['image_path'].str.replace('\\', '/') # Images are always in train/images/ self.images_dir = self.root_dir / "train" def __len__(self): return len(self.annotations) def __getitem__(self, idx): row = self.annotations.iloc[idx] img_path = self.images_dir / row['image_path'] image = Image.open(img_path).convert('RGB') label = row['label'] if self.transform: image = self.transform(image) return image, label # Example usage transform = transforms.Compose([ transforms.Resize((256, 256)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) ]) train_dataset = TIGASDataset("TIGAS", split='train', transform=transform) test_dataset = TIGASDataset("TIGAS", split='test', transform=transform) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4) test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4) ``` ### Using with TIGAS Model ```python from tigas import TIGAS # Initialize model (auto-downloads from HuggingFace) tigas = TIGAS(auto_download=True, device='cuda') # Evaluate on dataset images from pathlib import Path import pandas as pd data_root = Path("TIGAS") test_df = pd.read_csv(data_root / "test" / "annotations01.csv") test_df['image_path'] = test_df['image_path'].str.replace('\\', '/') # Evaluate first 10 images for i, row in test_df.head(10).iterrows(): img_path = data_root / "train" / row['image_path'] score = tigas(str(img_path)) true_label = "Real" if row['label'] == 0 else "Fake" pred_label = "Real" if score > 0.5 else "Fake" print(f"{img_path.name}: Score={score:.4f}, True={true_label}, Pred={pred_label}") ``` ## Generators Included ### Diffusion Models - **Stable Diffusion 1.4, 1.5, XL** - Open-source text-to-image diffusion models - **DALL-E 2** - OpenAI's text-to-image model - **Midjourney** - Commercial text-to-image service - **GLIDE** - OpenAI's guided language-to-image diffusion - **ADM** - Ablated Diffusion Model (class-conditional on ImageNet) - **VQDM** - Vector Quantized Diffusion Model ### GANs (Generative Adversarial Networks) - **BigGAN** - Large-scale class-conditional GAN - **GauGAN** - NVIDIA's semantic image synthesis - **StarGAN** - Multi-domain face manipulation ## Citation If you use this dataset in your research, please cite: ```bibtex @dataset{tigas_dataset_2025, title={TIGAS Dataset: A Comprehensive Collection for AI-Generated Image Detection}, author={Morgenshtern, Dmitrij}, year={2025}, publisher={HuggingFace}, url={https://huggingface.co/datasets/H1merka/TIGAS-dataset} } ``` ## License This dataset is released under the [MIT License](LICENSE). **Important**: Individual images in this dataset may be derived from or generated using various models with their own licensing terms: - ImageNet images (in `0_real` folders) are subject to [ImageNet terms of use](https://www.image-net.org/download.php) - Generated images are outputs of the respective models (Stable Diffusion, Midjourney, etc.) The annotations and dataset organization are MIT licensed. ## Related Resources - **TIGAS Model**: [huggingface.co/H1merka/TIGAS](https://huggingface.co/H1merka/TIGAS) - **GitHub Repository**: [github.com/H1merka/TIGAS](https://github.com/H1merka/TIGAS) ## Changelog - **v1.0** (December 2025): Initial release with 142,902 images from 19 sources