Published August 24, 2026 | Version 1.0

SegDeep Dataset: A Large-Scale Dataset and Benchmark for Semantic Segmentation of Diffusion-Based Facial Manipulations

  • 1. ROR icon Instituto Politécnico Nacional
  • 2. Escuela Superior de Ingeniería Mecánica y Eléctrica
  • 3. ROR icon Tecnológico de Monterrey

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

Files (11.5 GB)

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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