.png){kind=icon}
返回
Enhanced embedding capacity for the SMSD-based data-hiding method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.image.2019.07.013.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The embedding capacity and stego-image quality are two important features in data-hiding schemes. In 2016, Kuo et al. introduced sparse modified signed-digit (SMSD)-based data hiding that could embed secret data into a group of n cover-pixels. Compared with previous exploiting modification direction (EMD) and generalized EMD (GEMD)-based data hiding, SMSD data hiding has higher embedding capacity and better stego-image quality. In this paper, we introduce a new definition of the enhanced MSD (EMSD) representation and propose an EMSD-based data-hiding scheme that improves the embedding capacity of SMSD data hiding and maintains equal quality of the stego-image. In addition, the embedding capacity in the proposed scheme is further improved using the section-wise approach in EMSD data hiding.
Keyword:
Data hiding
Embedding capacity
Sparse MSD
Enhanced MSD
Section-wise
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
S
IF:
2.7
论文数:
2.8K
被引数:
4.2K
机构
引用论文
Phenotype Tracking of Leafy Greens Based on Weakly Supervised Instance Segmentation and Data Association基于弱监督实例分割和数据关联的绿叶蔬菜表型追踪
PREAI
分享收藏Enhance Embedding Capacity of Generalized Exploiting Modification Directions in Data Hiding.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Threshold changeable secret image sharing scheme based on interpolation polynomial基于插值多项式的阈值可变秘密图像共享方案
PREAI
分享收藏Digital Image Steganalysis Based on Visual Attention and Deep Reinforcement Learning基于视觉注意和深度强化学习的数字图像隐像分析IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏A cross-scaled simulation on cell inactivation efficacy of pulsed electric fields by leveraging percolation theory利用渗流理论研究脉冲电场细胞灭活效率的跨尺度模拟
PREAI
分享收藏
查看更多内容