105 research outputs found
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
Innovating with Artificial Intelligence: Capturing the Constructive Functional Capabilities of Deep Generative Learning
As an emerging species of artificial intelligence, deep generative learning models can generate an unprecedented variety of new outputs. Examples include the creation of music, text-to-image translation, or the imputation of missing data. Similar to other AI models that already evoke significant changes in society and economy, there is a need for structuring the constructive functional capabilities of DGL. To derive and discuss them, we conducted an extensive and structured literature review. Our results reveal a substantial scope of six constructive functional capabilities demonstrating that DGL is not exclusively used to generate unseen outputs. Our paper further guides companies in capturing and evaluating DGL’s potential for innovation. Besides, our paper fosters an understanding of DGL and provides a conceptual basis for further research
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
Open video data sharing in developmental and behavioural science
Video recording is a widely used method for documenting infant and child
behaviours in research and clinical practice. Video data has rarely been shared
due to ethical concerns of confidentiality, although the need of shared
large-scaled datasets remains increasing. This demand is even more imperative
when data-driven computer-based approaches are involved, such as screening
tools to complement clinical assessments. To share data while abiding by
privacy protection rules, a critical question arises whether efforts at data
de-identification reduce data utility? We addressed this question by showcasing
the Prechtl's general movements assessment (GMA), an established and globally
practised video-based diagnostic tool in early infancy for detecting
neurological deficits, such as cerebral palsy. To date, no shared
expert-annotated large data repositories for infant movement analyses exist.
Such datasets would massively benefit training and recalibration of human
assessors and the development of computer-based approaches. In the current
study, sequences from a prospective longitudinal infant cohort with a total of
19451 available general movements video snippets were randomly selected for
human clinical reasoning and computer-based analysis. We demonstrated for the
first time that pseudonymisation by face-blurring video recordings is a viable
approach. The video redaction did not affect classification accuracy for either
human assessors or computer vision methods, suggesting an adequate and
easy-to-apply solution for sharing movement video data. We call for further
explorations into efficient and privacy rule-conforming approaches for
deidentifying video data in scientific and clinical fields beyond movement
assessments. These approaches shall enable sharing and merging stand-alone
video datasets into large data pools to advance science and public health
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