Facial Attribute Based Text Guided Face Anonymization

TL;DR

This paper introduces a deep learning-based face anonymization method using diffusion models and text prompts to generate realistic, unrecognizable faces, aiding privacy-compliant dataset creation.

Contribution

It presents a novel face anonymization pipeline that leverages diffusion-based inpainting and text prompts, avoiding GAN training and enhancing privacy preservation.

Findings

Generates natural-looking anonymized faces with specified attributes

Eliminates the need for GAN training through diffusion models

Facilitates privacy-compliant dataset creation

Abstract

The increasing prevalence of computer vision applications necessitates handling vast amounts of visual data, often containing personal information. While this technology offers significant benefits, it should not compromise privacy. Data privacy regulations emphasize the need for individual consent for processing personal data, hindering researchers' ability to collect high-quality datasets containing the faces of the individuals. This paper presents a deep learning-based face anonymization pipeline to overcome this challenge. Unlike most of the existing methods, our method leverages recent advancements in diffusion-based inpainting models, eliminating the need for training Generative Adversarial Networks. The pipeline employs a three-stage approach: face detection with RetinaNet, feature extraction with VGG-Face, and realistic face generation using the state-of-the-art BrushNet diffusion model. BrushNet utilizes the entire image, face masks, and text prompts specifying desired facial attributes like age, ethnicity, gender, and expression. This enables the generation of natural-looking images with unrecognizable individuals, facilitating the creation of privacy-compliant datasets for computer vision research.