DeepPrivacy: A Generative Adversarial Network for Face Anonymization

Hukkelås, Håkon; Mester, Rudolf; Lindseth, Frank

doi:10.1007/978-3-030-33720-9_44

Cited by 166 publications

(205 citation statements)

References 23 publications

Supporting

Mentioning

183

Contrasting

Order By: Relevance

“…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%

“…However, because this control vector is the one-hot encoded vector, the range of the de-identification is limited in the training set. In [8], the authors proposed a method to preserve facial pose by using facial landmark detector to generate a new random face with the same pose. However, due to the aforementioned limitation in GAN-based methods, this method also would not assure that the generated random face is different from the input face.…”

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%

See 3 more Smart Citations

CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

2020

View full text Add to dashboard Cite

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.

show abstract

“…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%

See 2 more Smart Citations

CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

2020

View full text Add to dashboard Cite

show abstract

“…Recent advances in generative machine learning such as variational autoencoders (VAEs) and generative adversarial networks (GANs) are starting to be applied to the anonymization problem [18][19][20][21][22][23][24]. The main idea of the generative approach is to learn the salient characteristics of the data distribution and sample new (synthetic) individuals from the distribution.…”

Section: Introductionmentioning

confidence: 99%

Preservation of Anomalous Subgroups On Variational Autoencoder Transformed Data

Maina¹,

Bryant²,

Ogallo³

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the groups predicted odds ratio from the model and observed odds ratio from the data. We then perform anonymization using a variational autoencoder (VAE) to synthesize an entirely new dataset that would ideally be drawn from the distribution of the original data. We repeat the anomalous subgroup discovery task on the new data and compare it to what was identified pre-anonymization. We evaluated our approach using publicly available datasets from the financial industry. Our evaluation confirmed that the approach was able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset. Such a distinction was maintained while having distinctly different records between the synthetic and original dataset. Finally, we packed the above end-to-end process into what we call Utility Guaranteed Deep Privacy (UGDP) system. UGDP can be easily extended to onboard alternative generative approaches such as GANs to synthesize tabular data.

show abstract

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

Zhao

Liu

Zhu

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

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.

show abstract

DeepPrivacy: A Generative Adversarial Network for Face Anonymization

Cited by 166 publications

References 23 publications

CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

Preservation of Anomalous Subgroups On Variational Autoencoder Transformed Data

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

Contact Info

Product

Resources

About