license: cc
pretty_name: Semi-Truths
size_categories:
- 10K<n<100K
Semi-Truths: The Evaluation Sample
Recent efforts have developed AI-generated image detectors claiming robustness against various augmentations, but their effectiveness remains unclear. Can these systems detect varying degrees of augmentation? To address these questions, we introduce Semi-Truths, featuring 27,600 real images, 245,300 masks, and 850,200 AI-augmented images featuring varying degrees of targeted and localized edits, created using diverse augmentation methods, diffusion models, and data distributions. Each augmented image includes detailed metadata for standardized, targeted evaluation of detector robustness.
🚀 Leverage the Semi-Truths dataset to understand the sensitivities of the latest AI-augmented image detectors, to various sizes of edits and semantic changes!
Dataset Structure
The general structure of the Semi-Truths Dataset is as follows:
- The original, real image and mask data can be found in the folder
original - Augmented images created with Diffusion Inpainting are in
inpainting - Prompt-edited images are in the folder
p2p - Prompt-edited image masks, computed post-augmentation, are in the folder
p2p_masks - All metadata can be found in
metadata.csv, including labels, datasets, entities, augmentation methods, diffusion models, change metrics, and so on.
├── metadata.csv (Image, Mask, and Change Information)
├── original (Real Images/Mask Pairs)
│ ├── images
│ │ ├── ADE20K
│ │ ├── CelebAHQ
│ │ ├── CityScapes
│ │ ├── HumanParsing
│ │ ├── OpenImages
│ │ └── SUN_RGBD
│ └── masks
│ ├── ADE20K
│ ├── CelebAHQ
│ ├── CityScapes
│ ├── HumanParsing
│ ├── OpenImages
│ └── SUN_RGBD
├── inpainting (Augmented Images)
│ ├── ADE20K
│ ├── CelebAHQ
│ ├── CityScapes
│ ├── HumanParsing
│ ├── OpenImages
│ └── SUN_RGBD
└── p2p (Augmented Images)
├── ADE20K
├── CelebAHQ
├── CityScapes
├── HumanParsing
├── OpenImages
└── SUN_RGBD