A Systematical Solution for Face De-identification

Yang, Shufan; Wang, Wei; Cheng, Yuehua

doi:10.1007/978-3-030-86608-2_3

Cited by 7 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [26] proposed a model combining attribute untangling and generating network, which disturbed the identity and expression features of face images respectively to achieve the effect of face recognition. Ref.…”

Section: ⅱ Related Workmentioning

confidence: 99%

Online New Media Oriented Privacy Data Recognition Mechanism Based on Deep Learning

Liu¹,

Yu²

2023

J Multimed Inf Syst

View full text Add to dashboard Cite

Although the current face de-recognition algorithm can effectively protect the user's identity, it will change the original attributes of the image, destroy the visual effect, or change the characteristics of the image to varying degrees. Traditional methods such as pixelation and occlusion can effectively realize face de-recognition, but the processed image cannot be used for basic processing such as face statistics and expression recognition, which greatly destroys the usability of the image in the field of online new media. In order to preserve more attributes of the original image and reduce the changes of facial skin color and texture features caused by the above de-recognition algorithm's operation on the whole facial area, this paper proposes a privacy protection model of face image based on generative adversarial network and facial feature analysis. Our model realizes face recognition based on changing the original image as little as possible. Specifically, we identify and extract features from fixed parts in face images, including eyes, nose, mouth, eyebrows, and cheeks. Then, we designed a face privacy recognition and protection model based on conditional generative adversarial networks. We verify our proposed method on two most commonly used face datasets in face research, namely CelebFaces Attributes and Labeled Faces in the Wild, and the results show that our proposed method achieve better performance compared with other face privacy protection methods.

show abstract

Section: ⅱ Related Workmentioning

confidence: 99%

Online New Media Oriented Privacy Data Recognition Mechanism Based on Deep Learning

Liu¹,

Yu²

2023

J Multimed Inf Syst

View full text Add to dashboard Cite

show abstract

“…Early research on applying GANs for face de-identification began by applying parametric face models [62]. The GAN-based deidentification methods can be divided into two sets: (1) the methods which rely on conditional inpainting [30,55,60,61], and (2) the manipulating facial representation methods [10,19,37,38,65,66,68]. Nousi et al [49] proposed fine-tuning of deep auto-encoders to preserve utility and privacy.…”

Section: Gan-based Face De-identificationmentioning

confidence: 99%

Examining StyleGAN as a Utility-Preserving Face De-identification Method

Moosavi

Nilizadeh

2023

PoPETs

View full text Add to dashboard Cite

Several face de-identification methods have been proposed to preserve users’ privacy by obscuring their faces. These methods, however, can degrade the quality of photos, and they usually do not preserve the utility of faces, i.e., their age, gender, pose, and facial expression. Recently, advanced generative adversarial network models, such as StyleGAN [ 33], have been proposed, which generate realistic, high-quality imaginary faces. In this paper, we investigate the use of StyleGAN in generating de-identified faces through style mixing, where the styles or features of the target face and an auxiliary face get mixed to generate a de-identified face that carries the utilities of the target face. We examined this de-identification method for preserving utility and privacy by implementing several face detection, verification, and identification attacks and conducting a user study. The results from our extensive experiments, human evaluation, and comparison with two state-of-the-art face de-identification methods, i.e., CIAGAN and DeepPrivacy, show that StyleGAN performs on par or better than these methods, preserving users’ privacy and images’ utility. In particular, the results of the machine learning-based experiments show that StyleGAN0-4 preserves utility better than CIAGAN and DeepPrivacy while preserving privacy at the same level. StyleGAN 0-3 preserves utility at the same level while providing more privacy. In this paper, for the first time, we also performed a carefully designed user study to examine both privacy and utility-preserving properties of StyleGAN 0-3, 0-4, and 0-5, as well as CIAGAN and DeepPrivacy from the human observers’ perspectives. Our statistical tests showed that participants tend to verify and identify StyleGAN 0-5 images easier than DeepPrivacy images. All the methods but StyleGAN 0-5 had significantly lower identification rates than CIAGAN. Regarding utility, as expected, StyleGAN 0-5 performed significantly better in preserving some attributes. Among all methods, on average, participants believe gender has been preserved the most while naturalness has been preserved the least.

show abstract

“…To anonymize face images, the leading proposals use generative adversarial networks (GANs) [129] and differential privacy [130]. Several proposals use GANs to first transform face images into latent space vectors, modify those vectors to remove identity information, and reconstruct the images from the modified vectors [102], [17], [103]. The modified faces still look human but are anonymized to prevent accurate identification.…”

Section: B Anonymizing Faces 2bmentioning

confidence: 99%

SoK: Anti-Facial Recognition Technology

Wenger¹,

Shan²,

Zheng³

et al. 2021

Preprint

View full text Add to dashboard Cite

The rapid adoption of facial recognition (FR) technology by both government and commercial entities in recent years has raised concerns about civil liberties and privacy. In response, a broad suite of so-called "anti-facial recognition" (AFR) tools has been developed to help users avoid unwanted facial recognition. The set of AFR tools proposed in the last few years is wide-ranging and rapidly evolving, necessitating a step back to consider the broader design space of AFR systems and long-term challenges. This paper aims to fill that gap and provides the first comprehensive analysis of the AFR research landscape. Using the operational stages of FR systems as a starting point, we create a systematic framework for analyzing the benefits and tradeoffs of different AFR approaches. We then consider both technical and social challenges facing AFR tools and propose directions for future research in this field.

show abstract

A Systematical Solution for Face De-identification

Cited by 7 publications

References 12 publications

Online New Media Oriented Privacy Data Recognition Mechanism Based on Deep Learning

Online New Media Oriented Privacy Data Recognition Mechanism Based on Deep Learning

Examining StyleGAN as a Utility-Preserving Face De-identification Method

SoK: Anti-Facial Recognition Technology

Contact Info

Product

Resources

About