FDeID-Toolbox: Face De-Identification Toolbox

2026-03-13

AI Summary

Existing research on face de-identification lacks standardized implementations, evaluation protocols, and reproducibility, making fair comparisons across methods difficult. To address this gap, this work introduces a modular toolbox that provides, for the first time, a unified framework encompassing both classical and state-of-the-art generative models. The toolbox integrates standardized data loading, a diverse suite of de-identification algorithms—including current state-of-the-art approaches—flexible inference pipelines, and a multidimensional evaluation system that jointly assesses privacy preservation, attribute retention, and visual quality. Experimental results demonstrate that the proposed toolkit enables fair, reproducible comparisons under consistent experimental conditions, significantly enhancing research efficiency and result comparability in the field of face de-identification.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Face de-identification (FDeID) aims to remove personally identifiable information from facial images while preserving task-relevant utility attributes such as age, gender, and expression. It is critical for privacy-preserving computer vision, yet the field suffers from fragmented implementations, inconsistent evaluation protocols, and incomparable results across studies. These challenges stem from the inherent complexity of the task: FDeID spans multiple downstream applications (e.g., age estimation, gender recognition, expression analysis) and requires evaluation across three dimensions (e.g., privacy protection, utility preservation, and visual quality), making existing codebases difficult to use and extend. To address these issues, we present FDeID-Toolbox, a comprehensive toolbox designed for reproducible FDeID research. Our toolbox features a modular architecture comprising four core components: (1) standardized data loaders for mainstream benchmark datasets, (2) unified method implementations spanning classical approaches to SOTA generative models, (3) flexible inference pipelines, and (4) systematic evaluation protocols covering privacy, utility, and quality metrics. Through experiments, we demonstrate that FDeID-Toolbox enables fair and reproducible comparison of diverse FDeID methods under consistent conditions.

Problem

• Face de-identification
• privacy-preserving computer vision
• evaluation protocols
• reproducible research
• utility preservation

Innovation

• Face De-Identification
• Modular Toolbox
• Reproducible Research
• Privacy-Preserving Vision
• Unified Evaluation Protocol