scholarly journals A State-of-the-Art Survey of Coverless Text Information Hiding

2018 ◽  
Vol 10 (7) ◽  
pp. 52-58
Author(s):  
Shahbaz Ali ◽  
Keyword(s):  
Information Hiding ◽  
State Of The Art ◽  
Text Information

Study on the Technology of Information Hiding Based on HTML Tags

2013 ◽  
Vol 798-799 ◽  
pp. 423-426
Author(s):  
Xiao Feng Wang
Keyword(s):  
Information Hiding ◽  
Hiding Capacity ◽  
Comprehensive Utilization ◽  
New Methods ◽  
Text Information ◽  
Information Hiding Capacity

Through the analysis of the hypertext markup, proposed and implemented several new methods of text information hiding. The concealment of these methods is better, Comprehensive utilization of these methods can obtain large information hiding capacity, better concealed. And they have better robustness for traditional attack.

scholarly journals Information hiding techniques for infrared images: exploring the state-of-the art and challenges

2015 ◽  
Author(s):  
Victor Pomponiu ◽  
Davide Cavagnino ◽  
Marco Botta ◽  
Hossein Nejati
Keyword(s):  
Information Hiding ◽  
State Of The Art ◽  
The State ◽  
Infrared Images

scholarly journals Effective Representing of Information Network by Variational Autoencoder

2017 ◽  
Author(s):  
Hang Li ◽  
Haozheng Wang ◽  
Zhenglu Yang ◽  
Haochen Liu
Keyword(s):  
State Of The Art ◽  
Vector Model ◽  
Information Network ◽  
Network Representation ◽  
Linear Relationships ◽  
Variational Autoencoder ◽  
Benchmark Datasets ◽  
Text Information ◽  
Complex Features ◽  
Better Than

Network representation is the basis of many applications and of extensive interest in various fields, such as information retrieval, social network analysis, and recommendation systems. Most previous methods for network representation only consider the incomplete aspects of a problem, including link structure, node information, and partial integration. The present study proposes a deep network representation model that seamlessly integrates the text information and structure of a network. Our model captures highly non-linear relationships between nodes and complex features of a network by exploiting the variational autoencoder (VAE), which is a deep unsupervised generation algorithm. We also merge the representation learned with a paragraph vector model and that learned with the VAE to obtain the network representation that preserves both structure and text information. We conduct comprehensive empirical experiments on benchmark datasets and find our model performs better than state-of-the-art techniques by a large margin.

scholarly journals Coverless image steganography based on DenseNet feature mapping

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Qiang Liu ◽  
Xuyu Xiang ◽  
Jiaohua Qin ◽  
Yun Tan ◽  
Yao Qiu
Keyword(s):  
Information Hiding ◽  
Binary Tree ◽  
State Of The Art ◽  
Image Steganography ◽  
High Dimensional ◽  
Feature Mapping ◽  
Hidden Information ◽  
Geometric Attacks ◽  
Coverless Information Hiding ◽  
Cis Methods

Abstract Since the concept of coverless information hiding was proposed, it has been greatly developed due to its effectiveness of resisting the steganographic tools. Most existing coverless image steganography (CIS) methods achieve excellent robustness under non-geometric attacks. However, they do not perform well under some geometric attacks. Towards this goal, a CIS algorithm based on DenseNet feature mapping is proposed. Deep learning is introduced to extract high-dimensional CNN features which are mapped into hash sequences. For the sender, a binary tree hash index is built to accelerate index speed of searching hidden information and DenseNet hash sequence, and then, all matched images are sent. For the receiver, the secret information can be recovered successfully by calculating the DenseNet hash sequence of the cover image. During the whole steganography process, the cover images remain unchanged. Experimental results and analysis show that the proposed scheme has better robust compared with the state-of-the-art methods under geometric attacks.

The Survey on Applications of Information Hiding Technology in E-Commerce Security

2013 ◽  
Vol 433-435 ◽  
pp. 1866-1870
Author(s):  
Yan Mei Chai ◽  
Su Wen Zhu ◽  
Wen Ying Han
Keyword(s):  
Information Security ◽  
Research And Development ◽  
Information Hiding ◽  
Recent Progress ◽  
State Of The Art ◽  
Development Trends ◽  
Security Sector ◽  
Basic Model ◽  
Current State

The booming e-commerce industry is suffering from serious information security problems. As a potential and effective security solution, information hiding technology has been widely applied in many fields and drawn unprecedented attention. Based on our research, this paper provides a survey on the current state of the art information hiding technology, mainly covering the fundamental concepts, basic model, the recent progress of information hiding methods and its applications in e-commerce security sector. At last, possible research and development trends of information hiding technology are discussed.

scholarly journals Data Integration Using Through Attentive Multi-View Graph Auto-Encoders

2019 ◽  
pp. 344-349 ◽  
Author(s):  
S. Baskaran ◽  
P. Panchavarnam
Keyword(s):  
Data Integration ◽  
State Of The Art ◽  
Abstract Objects ◽  
Semantic Embedding ◽  
Semantic Data ◽  
Text Information

Thematic integration represents a function in likeness judgments of sets of objects which are unrelated taxonomically, like soup and scoop. We hypothesized that integration provides as an even more key method in the likeness evaluation of abstract objects due to their temporality, their big variability, and relational nature. One therapy is always to influence information from different options - such as for example text information -equally to coach visible designs and to constrain their predictions. We provide a fresh serious visual-semantic embedding design experienced to spot visible things applying equally marked picture information along with semantic data learned from unannotated text. We show this design fits state-of-the-art efficiency on the 1000-class ImageNet item acceptance concern while creating more semantically realistic problems, and also reveal that the semantic data may be used to create forecasts about thousands of picture brands maybe not seen throughout training. we design the integration applying multi-view chart auto-encoders, and include receptive process to ascertain the loads for every see regarding equivalent jobs and characteristics for greater interpretability. Our design has variable style for equally semi-supervised and unsupervised settings. Fresh benefits shown substantial predictive precision improvement. Situation reports also revealed greater design volume introduce node characteristics and interpretability.

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