用于图像隐写的深度学习技术的最新趋势

IF 0.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Vijay Kumar, Sahil Sharma, Chandan Kumar, A. Sahu
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引用次数: 4

摘要

深度卷积神经网络的发展在很大程度上是过去十年隐写术取得重大进展的原因。在图像隐写术领域,生成对抗性网络(GAN)越来越受欢迎。本研究描述了基于深度学习的图像隐写系统的发展现状。作者的目标是介绍使用深度学习技术在图像隐写术中所做的各种工作,并对各种方法进行一些说明。这项研究提出了一个结果,可以为未来基于图像隐写方法的深度学习研究开辟一些新的途径。今后可以探索这些新途径。此外,还列出了当前方法的优缺点,并提出了几个有希望的方向,以确定研究人员在未来研究途径中可以解决的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Latest Trends in Deep Learning Techniques for Image Steganography
The development of deep convolutional neural networks has been largely responsible for the significant strides forward made in steganography over the past decade. In the field of image steganography, generative adversarial networks (GAN) are becoming increasingly popular. This study describes current development in image steganographic systems based on deep learning. The authors' goal is to lay out the various works that have been done in image steganography using deep learning techniques and provide some notes on the various methods. This study proposed a result that could open up some new avenues for future research in deep learning based on image steganographic methods. These new avenues could be explored in the future. Moreover, the pros and cons of current methods are laid out with several promising directions to define problems that researchers can work on in future research avenues.
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来源期刊
International Journal of Digital Crime and Forensics
International Journal of Digital Crime and Forensics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
2.70
自引率
0.00%
发文量
15
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