AnonymousNet: Natural Face De-Identification With Measurable Privacy

Li, Tao; Lin, Lei

doi:10.1109/cvprw.2019.00013

Cited by 133 publications

(99 citation statements)

References 63 publications

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“…Emerging approaches are using deep learning-based generative models [5][6][7][8][9]. These methods have produced higher quality images thanks to deep generative models.…”

Section: Face De-identificationmentioning

confidence: 99%

“…These methods have produced higher quality images thanks to deep generative models. However, GAN-based methods [5,[7][8][9] have sometimes generated awkward facial images and cannot manipulate the amount of de-identification. In addition, randomly generated facial images may result in looking similar to the original one.…”

Section: Face De-identificationmentioning

confidence: 99%

“…The authors of [7] tried to preserve structural similarity index of image, i.e., luminance, contrast and structural differences. The method of [9] had a bit different perspective. The authors viewed identity as a combination of facial attributes.…”

Section: Face De-identificationmentioning

confidence: 99%

“…Regarding preservation of facial attributes while modifying personal identity, some research works use additional attribute classifiers or facial landmarks detector [5][6][7][8][9]. These methods can successfully preserve some facial attributes.…”

Section: Introductionmentioning

confidence: 99%

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CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

2020

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We live in an era of privacy concerns. As smart devices such as smartphones, service robots and surveillance cameras spread, preservation of our privacy becomes one of the major concerns in our daily life. Traditionally, the problem was resolved by simple approaches such as image masking or blurring. While these provide effective ways to remove identities from images, there are certain limitations when it comes to a matter of recognition from the processed images. For example, one may want to get ambient information from scenes even when privacy-related information such as facial appearance is removed or changed. To address the issue, our goal in this paper is not only to modify identity from faces but also keeps facial attributes such as color, pose and facial expression for further applications. We propose a novel face de-identification method based on a deep generative model in which we design the output vector from an encoder to be disentangled into two parts: identity-related part and the rest representing facial attributes. We show that by solely modifying the identity-related part from the latent vector, our method effectively modifies the facial identity to a completely new one while the other attributes that are loosely related to personal identity are preserved. To validate the proposed method, we provide results from experiments that measure two different aspects: effectiveness of personal identity modification and facial attribute preservation.

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“…Emerging approaches are using deep learning-based generative models [5][6][7][8][9]. These methods have produced higher quality images thanks to deep generative models.…”

Section: Face De-identificationmentioning

confidence: 99%

Section: Face De-identificationmentioning

confidence: 99%

Section: Face De-identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

2020

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“…It is capable of retaining structure similarity in the de-identified output based on a single input. Similarly, AnonymousNet [17] extracts facial features for structure but adds noise for GAN-generated images.…”

Section: Related Workmentioning

confidence: 99%

AnonFACES

Khan

Tsaloli

et al. 2020

Proceedings of the 19th Workshop on Privacy in the Electronic Society

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Private‐encoder: Enforcing privacy in latent space for human face images

Zhao

Liu

Zhu

et al. 2021

Concurrency and Computation

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The explosive growth of various computer vision technologies generates a tremendous amount of visual data online every day. In addition to bringing convenience and revolutionizing our daily life, image data also reveal a wide range of sensitive information and pose unprecedented privacy leakage risks. Particularly, in the case of photos contain human faces, people can easily access those face images on social media without any consent, and the misuse of personal information could cause serious privacy violation to individuals. Therefore, it is essential to consider sanitizing people's identity information when using images containing human faces. As a result, there has been rapid development in the area of facial anonymization, also called image de-identification. However, due to the emergence of numerous deep-learning based attacks, traditional anonymization methods such as blurring and mosaic are weak and ineffective to protect individual's privacy in face images. To respond to this challenge, this article proposes a novel de-identification method that utilizes a deep neural network. The proposed framework encompasses two modules: encoder network and generator network. The encoder transforms a face image into a high-semantic latent vector of codes, which will be de-identified according to the differential privacy criterion. The generator leverages the unconditional generative adversarial network to synthesize high-quality images based on the modified latent codes from the encoder.Extensive experimental results indicate that our proposed model can protect image privacy while keeping the processed image visual realistic.

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AnonymousNet: Natural Face De-Identification With Measurable Privacy

Cited by 133 publications

References 63 publications

CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

AnonFACES

Private‐encoder: Enforcing privacy in latent space for human face images

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