2,261 research outputs found
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
Facial landmarks constitute the most compressed representation of faces and
are known to preserve information such as pose, gender and facial structure
present in the faces. Several works exist that attempt to perform high-level
face-related analysis tasks based on landmarks. In contrast, in this work, an
attempt is made to tackle the inverse problem of synthesizing faces from their
respective landmarks. The primary aim of this work is to demonstrate that
information preserved by landmarks (gender in particular) can be further
accentuated by leveraging generative models to synthesize corresponding faces.
Though the problem is particularly challenging due to its ill-posed nature, we
believe that successful synthesis will enable several applications such as
boosting performance of high-level face related tasks using landmark points and
performing dataset augmentation. To this end, a novel face-synthesis method
known as Gender Preserving Generative Adversarial Network (GP-GAN) that is
guided by adversarial loss, perceptual loss and a gender preserving loss is
presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN
that leverages advantages of U-Net and DenseNet architectures. Extensive
experiments and comparison with recent methods are performed to verify the
effectiveness of the proposed method.Comment: 6 pages, 5 figures, this paper is accepted as 2018 24th International
Conference on Pattern Recognition (ICPR2018
VAE/WGAN-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images
We present a novel variational generative adversarial network (VGAN) based on
Wasserstein loss to learn a latent representation from a face image that is
invariant to identity but preserves head-pose information. This facilitates
synthesis of a realistic face image with the same head pose as a given input
image, but with a different identity. One application of this network is in
privacy-sensitive scenarios; after identity replacement in an image, utility,
such as head pose, can still be recovered. Extensive experimental validation on
synthetic and real human-face image datasets performed under 3 threat scenarios
confirms the ability of the proposed network to preserve head pose of the input
image, mask the input identity, and synthesize a good-quality realistic face
image of a desired identity. We also show that our network can be used to
perform pose-preserving identity morphing and identity-preserving pose
morphing. The proposed method improves over a recent state-of-the-art method in
terms of quantitative metrics as well as synthesized image quality.Comment: 6 pages, 5 figures, 2019 IEEE 29th International Workshop on Machine
Learning for Signal Processing (MLSP
On Face Segmentation, Face Swapping, and Face Perception
We show that even when face images are unconstrained and arbitrarily paired,
face swapping between them is actually quite simple. To this end, we make the
following contributions. (a) Instead of tailoring systems for face
segmentation, as others previously proposed, we show that a standard fully
convolutional network (FCN) can achieve remarkably fast and accurate
segmentations, provided that it is trained on a rich enough example set. For
this purpose, we describe novel data collection and generation routines which
provide challenging segmented face examples. (b) We use our segmentations to
enable robust face swapping under unprecedented conditions. (c) Unlike previous
work, our swapping is robust enough to allow for extensive quantitative tests.
To this end, we use the Labeled Faces in the Wild (LFW) benchmark and measure
the effect of intra- and inter-subject face swapping on recognition. We show
that our intra-subject swapped faces remain as recognizable as their sources,
testifying to the effectiveness of our method. In line with well known
perceptual studies, we show that better face swapping produces less
recognizable inter-subject results. This is the first time this effect was
quantitatively demonstrated for machine vision systems
My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization
Recently, productization of face recognition and identification algorithms
have become the most controversial topic about ethical AI. As new policies
around digital identities are formed, we introduce three face access models in
a hypothetical social network, where the user has the power to only appear in
photos they approve. Our approach eclipses current tagging systems and replaces
unapproved faces with quantitatively dissimilar deepfakes. In addition, we
propose new metrics specific for this task, where the deepfake is generated at
random with a guaranteed dissimilarity. We explain access models based on
strictness of the data flow, and discuss impact of each model on privacy,
usability, and performance. We evaluate our system on Facial Descriptor Dataset
as the real dataset, and two synthetic datasets with random and equal class
distributions. Running seven SOTA face recognizers on our results, MFMC reduces
the average accuracy by 61%. Lastly, we extensively analyze similarity metrics,
deepfake generators, and datasets in structural, visual, and generative spaces;
supporting the design choices and verifying the quality.Comment: 2023 IEEE Winter Conference on Applications of Computer Vision (WACV
Visual Content Privacy Protection: A Survey
Vision is the most important sense for people, and it is also one of the main
ways of cognition. As a result, people tend to utilize visual content to
capture and share their life experiences, which greatly facilitates the
transfer of information. Meanwhile, it also increases the risk of privacy
violations, e.g., an image or video can reveal different kinds of
privacy-sensitive information. Researchers have been working continuously to
develop targeted privacy protection solutions, and there are several surveys to
summarize them from certain perspectives. However, these surveys are either
problem-driven, scenario-specific, or technology-specific, making it difficult
for them to summarize the existing solutions in a macroscopic way. In this
survey, a framework that encompasses various concerns and solutions for visual
privacy is proposed, which allows for a macro understanding of privacy concerns
from a comprehensive level. It is based on the fact that privacy concerns have
corresponding adversaries, and divides privacy protection into three
categories, based on computer vision (CV) adversary, based on human vision (HV)
adversary, and based on CV \& HV adversary. For each category, we analyze the
characteristics of the main approaches to privacy protection, and then
systematically review representative solutions. Open challenges and future
directions for visual privacy protection are also discussed.Comment: 24 pages, 13 figure
Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools
The vast amount of data produced by today's medical imaging systems has led medical professionals to turn to novel technologies in order to efficiently handle their data and exploit the rich information present in them. In this context, artificial intelligence (AI) is emerging as one of the most prominent solutions, promising to revolutionise every day clinical practice and medical research. The pillar supporting the development of reliable and robust AI algorithms is the appropriate preparation of the medical images to be used by the AI-driven solutions. Here, we provide a comprehensive guide for the necessary steps to prepare medical images prior to developing or applying AI algorithms. The main steps involved in a typical medical image preparation pipeline include: (i) image acquisition at clinical sites, (ii) image de-identification to remove personal information and protect patient privacy, (iii) data curation to control for image and associated information quality, (iv) image storage, and (v) image annotation. There exists a plethora of open access tools to perform each of the aforementioned tasks and are hereby reviewed. Furthermore, we detail medical image repositories covering different organs and diseases. Such repositories are constantly increasing and enriched with the advent of big data. Lastly, we offer directions for future work in this rapidly evolving field
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