SegDeep Dataset: A Large-Scale Dataset and Benchmark for Semantic Segmentation of Diffusion-Based Facial Manipulations
Authors/Creators
Description
The dataset contains 28,245 facial images organized into three classes: Authentic images, DeepFake manipulations, and Face Swap manipulations. Authentic samples were obtained from the FFHQ dataset, while manipulated samples were generated using Stable Diffusion-based models. Pixel-level annotations were automatically generated during the manipulation process using a face-parsing model to identify the affected facial regions. The utility of the dataset was evaluated using different semantic segmentation architectures.
This dataset is a derivative work based on the Flickr-Faces-HQ (FFHQ) dataset. Appropriate credit is provided to the original FFHQ authors, and modifications introduced during the construction of the proposed dataset are documented
Files
real.zip
Additional details
Related works
- Is derived from
- Dataset: https://github.com/nvlabs/ffhq-dataset (Other)
- Is documented by
- Other: arXiv:1812.04948 (arXiv)
Funding
- Secretaria de Ciencia, Humanidades, Tecnología e Inovación
- Estancias Posdoctorales por México 2022(1) 744415
Dates
- Collected
-
2026-07-04
- Available
-
2026-07-24
Software
- Repository URL
- https://github.com/rodare903009-maker/SegDeep-Code/tree/main
- Programming language
- Python
- Development Status
- Active
References
- A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA) https://arxiv.org/abs/1812.04948