196 research outputs found
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
The unprecedented increase in the usage of computer vision technology in
society goes hand in hand with an increased concern in data privacy. In many
real-world scenarios like people tracking or action recognition, it is
important to be able to process the data while taking careful consideration in
protecting people's identity. We propose and develop CIAGAN, a model for image
and video anonymization based on conditional generative adversarial networks.
Our model is able to remove the identifying characteristics of faces and bodies
while producing high-quality images and videos that can be used for any
computer vision task, such as detection or tracking. Unlike previous methods,
we have full control over the de-identification (anonymization) procedure,
ensuring both anonymization as well as diversity. We compare our method to
several baselines and achieve state-of-the-art results.Comment: CVPR 202
DeepPrivacy: A Generative Adversarial Network for Face Anonymization
We propose a novel architecture which is able to automatically anonymize
faces in images while retaining the original data distribution. We ensure total
anonymization of all faces in an image by generating images exclusively on
privacy-safe information. Our model is based on a conditional generative
adversarial network, generating images considering the original pose and image
background. The conditional information enables us to generate highly realistic
faces with a seamless transition between the generated face and the existing
background. Furthermore, we introduce a diverse dataset of human faces,
including unconventional poses, occluded faces, and a vast variability in
backgrounds. Finally, we present experimental results reflecting the capability
of our model to anonymize images while preserving the data distribution, making
the data suitable for further training of deep learning models. As far as we
know, no other solution has been proposed that guarantees the anonymization of
faces while generating realistic images.Comment: Accepted to ISVC 201
Generative Adversarial Network based machine for fake data generation
This paper introduces a first approach on using Generative Adversarial Networks (GANs) for the generation of fake data, with the objective of anonymizing patients information in the health sector. This is intended to create valuable data that can be used both, in educational and research areas, while avoiding the risk of a sensitive data leakage. For this purpose, firstly a thorough research on GAN’s state of the art and available databases has been developed. The outcome of the project is a GAN system prototype adapted to generate raw data that imitates samples such as users variable status on hypothyroidism or a cardiogram report. The performance of this prototype has been checked and satisfactory results have been obtained for this first phase. Moreover, a novel research pathway has been opened so further research can be developed
GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions
In recent years, the increasing availability of personal data has raised
concerns regarding privacy and security. One of the critical processes to
address these concerns is data anonymization, which aims to protect individual
privacy and prevent the release of sensitive information. This research focuses
on the importance of face anonymization. Therefore, we introduce
GANonymization, a novel face anonymization framework with facial
expression-preserving abilities. Our approach is based on a high-level
representation of a face, which is synthesized into an anonymized version based
on a generative adversarial network (GAN). The effectiveness of the approach
was assessed by evaluating its performance in removing identifiable facial
attributes to increase the anonymity of the given individual face.
Additionally, the performance of preserving facial expressions was evaluated on
several affect recognition datasets and outperformed the state-of-the-art
methods in most categories. Finally, our approach was analyzed for its ability
to remove various facial traits, such as jewelry, hair color, and multiple
others. Here, it demonstrated reliable performance in removing these
attributes. Our results suggest that GANonymization is a promising approach for
anonymizing faces while preserving facial expressions.Comment: 26 pages, 11 figures, 6 tables, ACM Transactions on Multimedia
Computing, Communications, and Application
Anonymization for Skeleton Action Recognition
Skeleton-based action recognition attracts practitioners and researchers due
to the lightweight, compact nature of datasets. Compared with RGB-video-based
action recognition, skeleton-based action recognition is a safer way to protect
the privacy of subjects while having competitive recognition performance.
However, due to improvements in skeleton estimation algorithms as well as
motion- and depth-sensors, more details of motion characteristics can be
preserved in the skeleton dataset, leading to potential privacy leakage. To
investigate the potential privacy leakage from skeleton datasets, we first
train a classifier to categorize sensitive private information from
trajectories of joints. Our preliminary experiments show that the gender
classifier achieves 87% accuracy on average and the re-identification task
achieves 80% accuracy on average for three baseline models: Shift-GCN, MS-G3D,
and 2s-AGCN. We propose an adversarial anonymization algorithm to protect
potential privacy leakage from the skeleton dataset. Experimental results show
that an anonymized dataset can reduce the risk of privacy leakage while having
marginal effects on action recognition performance
Deep Generative Models: The winning key for large and easily accessible ECG datasets?
Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored
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