Obfuscation of images via differential privacy: From facial images to general images

Croft, William Lee; Sack, Jörg-Rüdiger; Shi, Wenzhong

doi:10.1007/s12083-021-01091-9

Cited by 26 publications

(11 citation statements)

References 68 publications

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“…Ref. [33] achieved the same effect as anonymization by blurring facial images through formal differential privacy. Ref.…”

Section: ⅱ Related Workmentioning

confidence: 92%

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

Liu¹,

Yu²

2023

J Multimed Inf Syst

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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.

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“…Ref. [33] achieved the same effect as anonymization by blurring facial images through formal differential privacy. Ref.…”

Section: ⅱ Related Workmentioning

confidence: 92%

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

Liu¹,

Yu²

2023

J Multimed Inf Syst

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“…However, these methods can significantly degrade the quality of the original images. Croft et al [9], Liu et al [10], and Li and Clifton [11] almost simultaneously proposed another LDP algorithm for images. Liu et al [10] showed a concrete implementation using GANs, whereas Croft et al [9] showed an abstract formulation.…”

Section: Related Workmentioning

confidence: 99%

Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models

Shibata,

Hanaoka,

Cao

et al. 2023

Applied Sciences

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Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images. To address this, we introduce DP-GLOW, a hybrid that combines the local differential privacy (LDP) algorithm with GLOW, one of the flow-based deep generative models. By applying a GLOW model, we disentangle the pixelwise correlation of images, which makes it difficult to protect privacy with straightforward LDP algorithms for images. Specifically, we map images to the latent vector of the GLOW model, where each element follows an independent normal distribution. We then apply the Laplace mechanism to this latent vector to achieve ϵ-LDP, which is one of the LDP algorithms. Moreover, we applied DP-GLOW to chest X-ray images to generate LDP images while preserving pathologies. The ϵ-LDP-processed chest X-ray images obtained with DP-GLOW indicate that we have obtained a powerful tool for releasing and using medical images for training AI.

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“…Experiments revealed that the PPAPNet performs remarkably in converting original images into high-quality deidentified images and resisting inversion attacks [42]. Reference [43] directly processed face images in the pixel space to realize DP, regardless of the image's distribution characteristics. On this premise, the exponential mechanism proposed can provide superior visual quality for image confusion using the Laplacian mechanism, along with strong universality.…”

Section: Related Workmentioning

confidence: 99%

A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance

et al. 2022

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In recent years, the security and privacy issues of face data in video surveillance have become one of the hotspots. How to protect privacy while maintaining the utility of monitored faces is a challenging problem. At present, most of the mainstream methods are suitable for maintaining data utility with respect to pre-defined criteria such as the structure similarity or shape of the face, which bears the criticism of poor versatility and adaptability. This paper proposes a novel generative framework called Quality Maintenance-Variational AutoEncoder (QM-VAE), which takes full advantage of existing privacy protection technologies. We innovatively add the loss of service quality to the loss function to ensure the generation of de-identified face images with guided quality preservation. The proposed model automatically adjusts the generated image according to the different service quality evaluators, so it is generic and efficient in different service scenarios, even some that have nothing to do with simple visual effects. We take facial expression recognition as an example to present experiments on the dataset CelebA to demonstrate the utility-preservation capabilities of QM-VAE. The experimental data show that QM-VAE has the highest quality retention rate of 86%. Compared with the existing method, QM-VAE generates de-identified face images with significantly improved utility and increases the effect by 6.7%.

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Obfuscation of images via differential privacy: From facial images to general images

Cited by 26 publications

References 68 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

Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models

A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance

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