当前位置: X-MOL 学术WIREs Data Mining Knowl. Discov. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Deep learning based image steganography: A review
WIREs Data Mining and Knowledge Discovery ( IF 7.8 ) Pub Date : 2022-11-17 , DOI: 10.1002/widm.1481
Mohd Arif Wani, Bisma Sultan

A review of the deep learning based image steganography techniques is presented in this paper. For completeness, the recent traditional steganography techniques are also discussed briefly. The three key parameters (security, embedding capacity, and invisibility) for measuring the quality of an image steganographic technique are described. Various steganography techniques, with emphasis on the above three key parameters, are reviewed. The steganography techniques are classified here into three main categories: Traditional, Hybrid, and fully Deep Learning. The hybrid techniques are further divided into three sub-categories: Cover Generation, Distortion Learning, and Adversarial Embedding. The fully Deep Learning techniques, based on the nature of the input, are further divided into three sub-categories: GAN Embedding, Embedding Less, and Category Label. The main ideas of the important deep learning based steganography techniques are described. The strong and weak features of these techniques are outlined. The results reported by researchers on benchmark data sets CelebA, Bossbase, PASCAL-VOC12, CIFAR-100, ImageNet, and USC-SIPI are used to evaluate the performance of various steganography techniques. Analysis of the results shows that there is scope for new suitable deep learning architectures that can improve the capacity and invisibility of image steganography.

中文翻译:

基于深度学习的图像隐写术:综述

本文对基于深度学习的图像隐写术技术进行了综述。为了完整起见,还简要讨论了最近的传统隐写术技术。描述了衡量图像隐写技术质量的三个关键参数(安全性、嵌入容量和不可见性)。回顾了各种隐写术技术,重点放在上述三个关键参数上。隐写术技术在这里分为三大类:传统、混合和完全深度学习。混合技术进一步分为三个子类别:覆盖生成、失真学习和对抗嵌入。基于输入的性质,完全深度学习技术进一步分为三个子类:GAN Embedding,Embedding Less,和类别标签。描述了重要的基于深度学习的隐写术技术的主要思想。概述了这些技术的优缺点。研究人员在基准数据集 CelebA、Bossbase、PASCAL-VOC12、CIFAR-100、ImageNet 和 USC-SIPI 上报告的结果用于评估各种隐写术技术的性能。结果分析表明,新的合适的深度学习架构可以提高图像隐写术的容量和不可见性。和 USC-SIPI 用于评估各种隐写技术的性能。结果分析表明,新的合适的深度学习架构可以提高图像隐写术的容量和不可见性。和 USC-SIPI 用于评估各种隐写技术的性能。结果分析表明,新的合适的深度学习架构可以提高图像隐写术的容量和不可见性。
更新日期:2022-11-17
down
wechat
bug