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--- |
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license: cc |
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pretty_name: Semi-Truths |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Semi-Truths: The Evaluation Sample # |
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**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?** |
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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. |
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Each augmented image includes detailed metadata for standardized, targeted evaluation of detector robustness. |
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🚀 Leverage the Semi-Truths dataset to understand the sensitivities of the latest AI-augmented image detectors, to various sizes of edits and semantic changes! |
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📂 **NOTE:** *This is a subset of the Semi-Truths dataset created for ease of evaluation of AI-Augmented image detectors. For users with memory contraints or initial exploration of Semi-Truths, we recommend using this dataset. |
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For the full dataset, please see `semi-truths/Semi-Truths`.* |
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<!--  --> |
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<centering><img src="https://cdn-uploads.huggingface.co/production/uploads/666454f1f99defe86aca3882/AaKKr-VDqcsml4sDcYLrh.png" alt="head_figure" width="800"/></centering> |
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<!-- ## Loading Dataset ## |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset('hoffman-lab/SkyScenes',name="H_35_P_45 images") |
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``` --> |
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## Directions ## |
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🌅 **I want to use the Semi-Truths dataset to evaluate my detector!** |
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* The `metadata.csv` file organizes all image file information under columns `image_id` and `image_path`. |
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* Leverage this information to pass both real and fake images to the detector you're evaluating. |
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* Append the detector predictions to the metadata file. |
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* Our metadata contains data attributes and various change metrics that describe the kind of augmentation that occured. |
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* By grouping predictions and computing metrics on images defined by a type of augmentation, you can gauge the specific strengths and weakness of the detecor! |
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To leverage our evaluation and analysis protocols, please visit our Github at: [Coming Soon! ⏳] |
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## Dataset Structure ## |
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The general structure of the Semi-Truths Dataset is as follows: |
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- The original, real image and mask data can be found in the folder `original` |
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- Augmented images created with Diffusion Inpainting are in `inpainting` |
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- Prompt-edited images are in the folder `p2p` |
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- Prompt-edited image masks, computed post-augmentation, are in the folder `p2p_masks` |
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- All metadata can be found in `metadata.csv`, including labels, datasets, entities, augmentation methods, diffusion models, change metrics, and so on. |
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``` |
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├── metadata.csv (Image, Mask, and Change Information) |
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├── original (Real Images/Mask Pairs) |
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│ ├── images |
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│ │ ├── ADE20K |
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│ │ ├── CelebAHQ |
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│ │ ├── CityScapes |
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│ │ ├── HumanParsing |
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│ │ ├── OpenImages |
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│ │ └── SUN_RGBD |
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│ └── masks |
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│ ├── ADE20K |
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│ ├── CelebAHQ |
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│ ├── CityScapes |
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│ ├── HumanParsing |
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│ ├── OpenImages |
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│ └── SUN_RGBD |
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├── inpainting (inpainted augmented images) |
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│ ├── ADE20K |
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│ ├── CelebAHQ |
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│ ├── CityScapes |
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│ ├── HumanParsing |
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│ ├── OpenImages |
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│ └── SUN_RGBD |
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└── p2p (prompt-based augmented images) |
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├── ADE20K |
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├── CelebAHQ |
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├── CityScapes |
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├── HumanParsing |
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├── OpenImages |
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└── SUN_RGBD |
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``` |
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# How to download Semi Truths? |
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You can download the whole dataset Semi Truths by cloning the dataset using the command: |
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git clone https://huggingface.co/datasets/semi-truths/Semi-Truths-Evalset |
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