--- license: cc pretty_name: Semi-Truths size_categories: - 10K head_figure **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 ```