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- <p align="center">
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- <img src="https://huggingface.co/datasets/WenhaoWang/ScaleDF/resolve/main/assets/teasor.png" width="800">
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  # Summary
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  This is the dataset proposed in our paper [**Scaling Laws for Deepfake Detection**](https://arxiv.org/abs/2510.16320).
 
 
 
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  ScaleDF is the largest dataset in the deepfake detection domain to date. It contains over 5.8 million real images from 51 different datasets (domains) and more than 8.8 million fake images generated by 102 deepfake methods.
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  # Directory
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  ```
 
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  size_categories:
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  - 10M<n<100M
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  ---
 
 
 
 
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  # Summary
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  This is the dataset proposed in our paper [**Scaling Laws for Deepfake Detection**](https://arxiv.org/abs/2510.16320).
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+ <p align="center">
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+ <img src="https://huggingface.co/datasets/WenhaoWang/ScaleDF/resolve/main/assets/teasor.png" width="800">
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+ </p>
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  ScaleDF is the largest dataset in the deepfake detection domain to date. It contains over 5.8 million real images from 51 different datasets (domains) and more than 8.8 million fake images generated by 102 deepfake methods.
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+ Using ScaleDF, we observe power-law scaling similar to that shown in large language models (LLMs). Specifically, the average detection error follows a predictable power-law decay as either the number of real domains or the number of deepfake methods increases.
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  # Directory
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  ```