Coverless Information Hiding Method Based on Multi-keywords

2016 ◽  
pp. 39-47 ◽  
Author(s):  
Zhili Zhou ◽  
Yan Mu ◽  
Ningsheng Zhao ◽  
Q. M. Jonathan Wu ◽  
Ching-Nung Yang
Keyword(s):  
Information Hiding ◽  
Coverless Information Hiding

scholarly journals Coverless information hiding based on the generation of anime characters

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Yi Cao ◽  
Zhili Zhou ◽  
Q. M. Jonathan Wu ◽  
Chengsheng Yuan ◽  
Xingming Sun
Keyword(s):  
Information Hiding ◽  
Coverless Information Hiding

An encrypted coverless information hiding method based on generative models

2021 ◽  
Vol 553 ◽  
pp. 19-30
Author(s):  
Qi Li ◽  
Xingyuan Wang ◽  
Xiaoyu Wang ◽  
Bin Ma ◽  
Chunpeng Wang ◽  
...  
Keyword(s):  
Information Hiding ◽  
Generative Models ◽  
Coverless Information Hiding

scholarly journals A Coverless Information Hiding Algorithm Based on Grayscale Gradient Co-occurrence Matrix

2018 ◽  
Vol 35 (sup1) ◽  
pp. 23-33 ◽  
Author(s):  
Jianbin Wu ◽  
Yiwen Liu ◽  
Zhenwei Dai ◽  
Ziyang Kang ◽  
Saman Rahbar ◽  
...  
Keyword(s):  
Information Hiding ◽  
Occurrence Matrix ◽  
Coverless Information Hiding

scholarly journals A Robust Text Coverless Information Hiding Based on Multi-Index Method

2021 ◽  
Vol 29 (3) ◽  
pp. 899-914
Author(s):  
Lin Xiang ◽  
Jiaohua Qin ◽  
Xuyu Xiang ◽  
Yun Tan ◽  
Neal N. Xiong
Keyword(s):  
Information Hiding ◽  
Index Method ◽  
Multi Index ◽  
Coverless Information Hiding

A Coverless Image Information Hiding Algorithm Based on Fractal Theory

2020 ◽  
Vol 30 (04) ◽  
pp. 2050062
Author(s):  
Xiang Zhang ◽  
Fei Peng ◽  
Zixing Lin ◽  
Min Long
Keyword(s):  
Information Hiding ◽  
Fractal Theory ◽  
Covert Communication ◽  
Generation Process ◽  
Image Generation ◽  
Generation Algorithm ◽  
Image Information ◽  
Fractal Image ◽  
Coverless Information Hiding ◽  
The Relationship

To improve the robustness and imperceptibility of the existing coverless image information hiding, a generative coverless image information hiding algorithm based on fractal theory is proposed in this paper. Firstly, four fractal image generation methods are analyzed, and the relationship between the coverless information hiding and these methods is discussed. Secondly, based on the fractal image generation algorithm, secret information is hidden by controlling pixel rendering during the generation process. The robustness, imperceptibility, and capability of resisting steganalysis are balanced by adjusting the rendering distance. As it directly generates stego images, this can resist the detection of most existing steganalysis methods. Meanwhile, different capacities can be achieved by adjusting the size of the generated image. Experimental results and analysis show that the proposed scheme can effectively resist steganalysis and has good robustness against various image attacks. Furthermore, it can achieve large capacity, and it has broad prospects for covert communication.

scholarly journals A Novel Coverless Information Hiding Method Based on the Most Significant Bit of the Cover Image

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 108579-108591
Author(s):  
Lina Yang ◽  
Haiyu Deng ◽  
Xiaocui Dang
Keyword(s):  
Information Hiding ◽  
Cover Image ◽  
Coverless Information Hiding

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.

scholarly journals Faster-RCNN Based Robust Coverless Information Hiding System in Cloud Environment

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 179891-179897 ◽  
Author(s):  
Zhili Zhou ◽  
Yi Cao ◽  
Meimin Wang ◽  
Enming Fan ◽  
Q. M. Jonathan Wu
Keyword(s):  
Information Hiding ◽  
Cloud Environment ◽  
Coverless Information Hiding
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