ArtiFact: Real and Fake Image Dataset
ArtiFact: Real and Fake Image Dataset
Datasets
ArtiFact: Real and Fake Image Dataset
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ArtiFact: Real and Fake Image Dataset
Use Case
ArtiFact: Real and Fake Image Dataset
Description
The ArtiFact dataset is a comprehensive collection of 2.5 million real and synthetic images across diverse categories. Specifically designed for evaluating synthetic image detectors, it includes images generated using 25 methods, such as GANs and diffusion models, thus providing a robust benchmark for real-world image detection tasks.
Description:
The ArtiFact dataset is a comprehensive, large-scale image collection that includes a diverse array of both real and synthetic images from multiple categories. These categories encompass Human/Human Faces, Animal/Animal Faces, Places, Vehicles, Art, and a variety of other real-life objects. Importantly, the dataset comprises images from 8 carefully selected sources to ensure diversity. Moreover, it features images synthesized using 25 distinct methods, including 13 GANs, 7 Diffusion models, and 5 other miscellaneous generators. In total, the dataset contains 2,496,738 images, consisting of 964,989 real images and 1,531,749 fake images.
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To maintain diversity across different sources, the dataset randomly samples real images from source datasets with numerous categories. Meanwhile, it generates synthetic images to match these categories. Furthermore, it uses captions and image masks from the COCO dataset to generate images via text-to-image and inpainting generators. Conversely, noise-to-image generators use normally distributed noise with various random seeds. To enhance realism, the dataset further processes the images to reflect real-world scenarios by applying random cropping, downscaling, and JPEG compression, all in accordance with IEEE VIP Cup 2022 standards.
The primary goal of the ArtiFact dataset is to serve as a benchmark for evaluating the performance of synthetic image detectors under real-world conditions. With its broad spectrum of diversity in terms of the generators used and levels of syntheticity, it provides a challenging environment for image detection tasks. Consequently, it pushes the boundaries of current detection capabilities.
Dataset Composition and Statistics
The dataset is carefully structured to ensure diversity and balance. In particular, it includes:
- Total Images: 2,496,738
- Real Images: 964,989
- Synthetic Images: 1,531,749
- Image Resolution: 200 × 200 pixels
The dataset spans multiple categories, including:
- Human and human faces
- Animals and animal faces
- Places and environments
- Vehicles
- Art and other real-world objects
Synthetic Image Generation Methods
A key strength of the dataset lies in its variety of image generation techniques. Specifically, synthetic images are created using 25 different methods, including:
- 13 GAN-based models
- 7 diffusion-based models
- 5 additional generation techniques
Key Features of the Dataset
- Large-scale dataset with over 2.4 million images
- Combination of real and synthetic image data
- Multiple categories covering real-world scenarios
- Advanced generation techniques for synthetic data
- Preprocessing aligned with real-world conditions
Applications and Use Cases
The ArtiFact Dataset can be used in several advanced applications. For instance:
- Deepfake Detection: Identify AI-generated or manipulated images
- Content Verification Systems: Detect fake media content
- Computer Vision Research: Benchmark detection algorithms
- AI Safety Systems: Improve trust in digital media
Conclusion
The ArtiFact Dataset is a powerful benchmark for synthetic image detection in real-world environments. Overall, it provides diverse and large-scale data for training advanced models. More importantly, it supports the development of reliable AI systems capable of distinguishing between real and generated content.
This dataset is sourced from Kaggle.
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