103,252 research outputs found
HFORD: High-Fidelity and Occlusion-Robust De-identification for Face Privacy Protection
With the popularity of smart devices and the development of computer vision
technology, concerns about face privacy protection are growing. The face
de-identification technique is a practical way to solve the identity protection
problem. The existing facial de-identification methods have revealed several
problems, including the impact on the realism of anonymized results when faced
with occlusions and the inability to maintain identity-irrelevant details in
anonymized results. We present a High-Fidelity and Occlusion-Robust
De-identification (HFORD) method to deal with these issues. This approach can
disentangle identities and attributes while preserving image-specific details
such as background, facial features (e.g., wrinkles), and lighting, even in
occluded scenes. To disentangle the latent codes in the GAN inversion space, we
introduce an Identity Disentanglement Module (IDM). This module selects the
latent codes that are closely related to the identity. It further separates the
latent codes into identity-related codes and attribute-related codes, enabling
the network to preserve attributes while only modifying the identity. To ensure
the preservation of image details and enhance the network's robustness to
occlusions, we propose an Attribute Retention Module (ARM). This module
adaptively preserves identity-irrelevant details and facial occlusions and
blends them into the generated results in a modulated manner. Extensive
experiments show that our method has higher quality, better detail fidelity,
and stronger occlusion robustness than other face de-identification methods
StyleGAN as a Utility-Preserving Face De-identification Method
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, GANs, such as StyleGAN, 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. 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 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. StyleGAN0-3 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 StyleGAN0-3, 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
StyleGAN0-5 images more easily than DeepPrivacy images. All the methods but
StyleGAN0-5 had significantly lower identification rates than CIAGAN. Regarding
utility, as expected, StyleGAN0-5 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
Learning Privacy Preserving Encodings through Adversarial Training
We present a framework to learn privacy-preserving encodings of images that
inhibit inference of chosen private attributes, while allowing recovery of
other desirable information. Rather than simply inhibiting a given fixed
pre-trained estimator, our goal is that an estimator be unable to learn to
accurately predict the private attributes even with knowledge of the encoding
function. We use a natural adversarial optimization-based formulation for
this---training the encoding function against a classifier for the private
attribute, with both modeled as deep neural networks. The key contribution of
our work is a stable and convergent optimization approach that is successful at
learning an encoder with our desired properties---maintaining utility while
inhibiting inference of private attributes, not just within the adversarial
optimization, but also by classifiers that are trained after the encoder is
fixed. We adopt a rigorous experimental protocol for verification wherein
classifiers are trained exhaustively till saturation on the fixed encoders. We
evaluate our approach on tasks of real-world complexity---learning
high-dimensional encodings that inhibit detection of different scene
categories---and find that it yields encoders that are resilient at maintaining
privacy.Comment: To appear in WACV 201
k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training
For a data holder, such as a hospital or a government entity, who has a
privately held collection of personal data, in which the revealing and/or
processing of the personal identifiable data is restricted and prohibited by
law. Then, "how can we ensure the data holder does conceal the identity of each
individual in the imagery of personal data while still preserving certain
useful aspects of the data after de-identification?" becomes a challenge issue.
In this work, we propose an approach towards high-resolution facial image
de-identification, called k-Same-Siamese-GAN, which leverages the
k-Same-Anonymity mechanism, the Generative Adversarial Network, and the
hyperparameter tuning methods. Moreover, to speed up model training and reduce
memory consumption, the mixed precision training technique is also applied to
make kSS-GAN provide guarantees regarding privacy protection on close-form
identities and be trained much more efficiently as well. Finally, to validate
its applicability, the proposed work has been applied to actual datasets - RafD
and CelebA for performance testing. Besides protecting privacy of
high-resolution facial images, the proposed system is also justified for its
ability in automating parameter tuning and breaking through the limitation of
the number of adjustable parameters
Prominent Attribute Modification using Attribute Dependent Generative Adversarial Network
Modifying the facial images with desired attributes is important, though
challenging tasks in computer vision, where it aims to modify single or
multiple attributes of the face image. Some of the existing methods are either
based on attribute independent approaches where the modification is done in the
latent representation or attribute dependent approaches. The attribute
independent methods are limited in performance as they require the desired
paired data for changing the desired attributes. Secondly, the attribute
independent constraint may result in the loss of information and, hence, fail
in generating the required attributes in the face image. In contrast, the
attribute dependent approaches are effective as these approaches are capable of
modifying the required features along with preserving the information in the
given image. However, attribute dependent approaches are sensitive and require
a careful model design in generating high-quality results. To address this
problem, we propose an attribute dependent face modification approach. The
proposed approach is based on two generators and two discriminators that
utilize the binary as well as the real representation of the attributes and, in
return, generate high-quality attribute modification results. Experiments on
the CelebA dataset show that our method effectively performs the multiple
attribute editing with preserving other facial details intactly
Retaining Expression on De-identified Faces
© Springer International Publishing AG 2017The extensive use of video surveillance along with advances in face recognition has ignited concerns about the privacy of the people identifiable in the recorded documents. A face de-identification algorithm, named k-Same, has been proposed by prior research and guarantees to thwart face recognition software. However, like many previous attempts in face de-identification, kSame fails to preserve the utility such as gender and expression of the original data. To overcome this, a new algorithm is proposed here to preserve data utility as well as protect privacy. In terms of utility preservation, this new algorithm is capable of preserving not only the category of the facial expression (e.g., happy or sad) but also the intensity of the expression. This new algorithm for face de-identification possesses a great potential especially with real-world images and videos as each facial expression in real life is a continuous motion consisting of images of the same expression with various degrees of intensity.Peer reviewe
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