Special Issue "Information Hiding and Multimedia Security"

Submission Deadline: 15 March 2020 (closed)
Guest Editors
Prof. Chin-Chen Chang, Feng Chia University, Taiwan
Prof. Dan Feng, Huazhong University of Science and Technology, China
Prof. Yongfeng Huang, Tsinghua University, China


The development of the Internet, the popularity of the e-government and e-commerce pose new challenges to the secure storage, secure transmission and secure access of information. As a new technology to ensure information security, information hiding has attracted many scholars' interest and has become a research hotspot all over the world. This special issue aims to provide an academic platform for researchers in this field to exchange new ideas, new methods, and new technologies. Submissions on information hiding and related information security topics are welcomed. The excellent papers of 15th Chinese national Information Hiding and multimedia information security Workshop (CIHW2019) will be considered for inclusion in the Special Issue. All submitted papers will undergo the Journal's standard peer-review process. The official website of CIHW is http://www.cihw.org.cn

Topics of interests include, but are not limited to:
• Information hiding theory and model;

• Steganography and steganalysis;

• Digital watermarking and digital rights management (DRM);

• Digital watermarking technology and anti-counterfeiting;

• Unconventional carrier information hiding;

• Digital forensics;

• Software protection;

• Multimedia data retrieval and certification;

• Wireless communication security;

• Data transmission security;

• Information content security;

• Encrypted domain signal processing;

• Multimedia security in cloud computing;

• Big data security privacy protection;

• Cryptography.

Information Hiding; Steganography; Steganalysis; Multimedia Security

Published Papers
  • 3D Multilayered Turtle Shell Models for Image Steganography
  • Abstract By embedding secret data into cover images, image steganography can produce non-discriminable stego-images. The turtle shell model for data hiding is an excellent method that uses a reference matrix to make a good balance between image quality and embedding capacity. However, increasing the embedding capacity by extending the area of basic structures of the turtle shell model usually leads to severe degradation of image quality. In this research, we innovatively extend the basic structure of the turtle shell model into a three-dimensional (3D) space. Some intrinsic properties of the original turtle shell model are well preserved in the 3D version.… More
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  • Image Information Hiding Method Based on Image Compression and Deep Neural Network
  • Abstract Image steganography is a technique that hides secret information into the cover image to protect information security. The current image steganography is mainly to embed a smaller secret image in an area such as a texture of a larger-sized cover image, which will cause the size of the secret image to be much smaller than the cover image. Therefore, the problem of small steganographic capacity needs to be solved urgently. This paper proposes a steganography framework that combines image compression. In this framework, the Vector Quantized Variational AutoEncoder (VQ-VAE) is used to achieve the compression of the secret image. The… More
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  • Constructive Texture Steganography Based on Compression Mapping of Secret Messages
  • Abstract This paper proposes a new constructive texture synthesis steganographic scheme by compressing original secret messages. First, we divide the original message into multiple bit blocks, which are transferred to decimal values and compressed into small decimal values by recording their interval sign characters. Then, a candidate pattern is generated by combining the given source pattern and boundary extension algorithm. Furthermore, we segment the candidate pattern into multiple candidate patches and use affine transformation algorithm to locate secret positions on a blank canvas, which are used to hide the sign characters by mapping the candidate patches. Finally, we select the candidate… More
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