Magnetic Resonance Imaging (MRI) is a vital modality for gaining precise
anatomical information, and it plays a significant role in medical imaging for
diagnosis and therapy planning. Image synthesis problems have seen a revolution
in recent years due to the introduction of deep learning techniques,
specifically Generative Adversarial Networks (GANs). This work investigates the
use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing
high-fidelity and realistic MRI image slices. The suggested approach uses a
dataset with a variety of brain MRI scans to train a DCGAN architecture. While
the discriminator network discerns between created and real slices, the
generator network learns to synthesise realistic MRI image slices. The
generator refines its capacity to generate slices that closely mimic real MRI
data through an adversarial training approach. The outcomes demonstrate that
the DCGAN promise for a range of uses in medical imaging research, since they
show that it can effectively produce MRI image slices if we train them for a
consequent number of epochs. This work adds to the expanding corpus of research
on the application of deep learning techniques for medical image synthesis. The
slices that are could be produced possess the capability to enhance datasets,
provide data augmentation in the training of deep learning models, as well as a
number of functions are made available to make MRI data cleaning easier, and a
three ready to use and clean dataset on the major anatomical plans.

使用深度卷积生成对抗网络（DCGAN）合成高保真和逼真的 MRI 图像切片，为医学影像研究提供深度学习技术的应用。

合成脑图像：用生成对抗模型弥合脑部映射中的差距

Synthetic Brain Images: Bridging the Gap in Brain Mapping With  Generative Adversarial Model

In this paper we propose a deep architecture for detecting people attributes
(e.g. gender, race, clothing ...) in surveillance contexts. Our proposal
explicitly deal with poor resolution and occlusion issues that often occur in
surveillance footages by enhancing the images by means of Deep Convolutional
Generative Adversarial Networks (DCGAN). Experiments show that by combining
both our Generative Reconstruction and Deep Attribute Classification Network we
can effectively extract attributes even when resolution is poor and in presence
of strong occlusions up to 80\% of the whole person figure.

本文提出了一个用于在监控环境中检测人员属性（如性别、种族、服装等）的深度架构，并通过深度卷积生成对抗网络（DCGAN）增强图像来处理监控拍摄中经常出现的低分辨率和遮挡问题。实验证明，结合我们的生成重构和深度属性分类网络，即使分辨率很低且整个人物图像有强烈的遮挡，我们也能有效地提取属性。

监控中基于生成对抗模型的人员属性识别

Generative Adversarial Models for People Attribute Recognition in  Surveillance

Since its appearance, Generative Adversarial Networks (GANs) have received a
lot of interest in the AI community. In image generation several projects
showed how GANs are able to generate photorealistic images but the results so
far did not look adequate for the quality standard of visual media production
industry. We present an optimized image generation process based on a Deep
Convolutional Generative Adversarial Networks (DCGANs), in order to create
photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore,
the system was fed with a limited dataset of images, less than two thousand
images. All these results give more clue about future exploitation of GANs in
Computer Graphics and Visual Effects.

本研究介绍一种基于深度卷积生成对抗网络的图像生成方法，能够使用少于两千张图像实现高分辨率（最大 1024x1024 像素）的真实感图像生成，未来可应用于计算机图形学和视觉效果。

使用生成对抗网络创建百万像素大小的图像

Megapixel Size Image Creation using Generative Adversarial Networks

Steganography is collection of methods to hide secret information ("payload")
within non-secret information "container"). Its counterpart, Steganalysis, is
the practice of determining if a message contains a hidden payload, and
recovering it if possible. Presence of hidden payloads is typically detected by
a binary classifier. In the present study, we propose a new model for
generating image-like containers based on Deep Convolutional Generative
Adversarial Networks (DCGAN). This approach allows to generate more
setganalysis-secure message embedding using standard steganography algorithms.
Experiment results demonstrate that the new model successfully deceives the
steganography analyzer, and for this reason, can be used in steganographic
applications.

本研究提出了一种基于深度卷积生成式对抗网络（Deep Convolutional Generative Adversarial Networks）的生成更安全的隐秘嵌入图像和标准隐写算法的方法，实验结果表明该方法成功地欺骗了隐写分析器，并可用于隐写应用。