DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis

TL;DR

DiffusionFace introduces a comprehensive, high-quality dataset of diffusion-based facial forgeries, enabling improved detection methods and addressing limitations of previous datasets in face forgery research.

Contribution

The paper presents the first diffusion-based face forgery dataset with extensive models, high-quality images, and evaluation protocols to advance detection techniques.

Findings

The dataset includes 11 diffusion models covering various forgery categories.

High-quality images with detailed metadata facilitate robust evaluation.

Practical protocols improve the assessment of forgery detection models.

Abstract

The rapid progress in deep learning has given rise to hyper-realistic facial forgery methods, leading to concerns related to misinformation and security risks. Existing face forgery datasets have limitations in generating high-quality facial images and addressing the challenges posed by evolving generative techniques. To combat this, we present DiffusionFace, the first diffusion-based face forgery dataset, covering various forgery categories, including unconditional and Text Guide facial image generation, Img2Img, Inpaint, and Diffusion-based facial exchange algorithms. Our DiffusionFace dataset stands out with its extensive collection of 11 diffusion models and the high-quality of the generated images, providing essential metadata and a real-world internet-sourced forgery facial image dataset for evaluation. Additionally, we provide an in-depth analysis of the data and introduce practical evaluation protocols to rigorously assess discriminative models' effectiveness in detecting counterfeit facial images, aiming to enhance security in facial image authentication processes. The dataset is available for download at \url{https://github.com/Rapisurazurite/DiffFace}.