Special Issue "Entropy Based Data Hiding and Its Applications"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: 10 July 2022.

Special Issue Editors

Dr. Tzu Chuen Lu
E-Mail Website
Guest Editor
Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
Interests: information hiding; steganography; image processing; interactive game design; 3D modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. David Megías
E-Mail Website
Guest Editor
Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Parc Mediterrani de la Tecnologia (edifici B3), Av. Carl Friedrich Gauss, 5, 08860 Castelldefels, Spain
Interests: information security and privacy; copyright protection; multimedia content (digital image, audio and video); watermarking; fingerprinting; steganography; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Data hiding techniques have been widely used in many human-centric applications, for example, secret sharing, tempered detection and recovery, copyright protection, data integrity, covert communication, authentication, and so on. In a hiding scheme, multimedia content such as text, audio, image, video, and compressed code could be used as the cover media to carry the secret message or watermark for generating the stegomedia. Data hiding techniques become more and more important in providing multimedia security.

Researchers have proposed a lot of state-of-the-art hiding schemes. Many of the schemes attempt to find optimal performance by applying concepts of information theory or entropy. These kinds of schemes adopt entropy theory to find proper places to modify the pixel or coefficient for concealing the secret message into the cover media.

The goal of this Special Issue is to concentrate on (but not limited to) the improvement of data hiding algorithms through information entropy, and on the application of entropy in real-world data hiding techniques. It will bring together researchers and practitioners from different research fields including data hiding, signal processing, cryptography or information theory, among others, to contribute with original research outcomes that address issues in data hiding algorithms using information theory approaches.

Keywords

  • steganography
  • digital watermarking
  • information hiding
  • coverless data hiding
  • reversible data hiding and applications
  • ownership proof/copyright protection
  • visual cryptography
  • embedding capacity
  • signal processing
  • embedding capacity
  • emerging applications of data hiding in IoT and big data
  • extraction/detection
  • data integrity

Published Papers (2 papers)

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Research

Article
Reversible Data Hiding in Encrypted Image Based on (7, 4) Hamming Code and UnitSmooth Detection
Entropy 2021, 23(7), 790; https://0-doi-org.brum.beds.ac.uk/10.3390/e23070790 - 22 Jun 2021
Viewed by 459
Abstract
With the development of cloud storage and privacy protection, reversible data hiding in encrypted images (RDHEI) plays the dual role of privacy protection and secret information transmission. RDHEI has a good application prospect and practical value. The current RDHEI algorithms still have room [...] Read more.
With the development of cloud storage and privacy protection, reversible data hiding in encrypted images (RDHEI) plays the dual role of privacy protection and secret information transmission. RDHEI has a good application prospect and practical value. The current RDHEI algorithms still have room for improvement in terms of hiding capacity, security and separability. Based on (7, 4) Hamming Code and our proposed prediction/ detection functions, this paper proposes a Hamming Code and UnitSmooth detection based RDHEI scheme, called HUD-RDHEI scheme for short. To prove our performance, two database sets—BOWS-2 and BOSSBase—have been used in the experiments, and peak signal to noise ratio (PSNR) and pure embedding rate (ER) are served as criteria to evaluate the performance on image quality and hiding capacity. Experimental results confirm that the average pure ER with our proposed scheme is up to 2.556 bpp and 2.530 bpp under BOSSBase and BOWS-2, respectively. At the same time, security and separability is guaranteed. Moreover, there are no incorrect extracted bits during data extraction phase and the visual quality of directly decrypted image is exactly the same as the cover image. Full article
(This article belongs to the Special Issue Entropy Based Data Hiding and Its Applications)
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Article
Improving the Reversible LSB Matching Scheme Based on the Likelihood Re-Encoding Strategy
Entropy 2021, 23(5), 577; https://0-doi-org.brum.beds.ac.uk/10.3390/e23050577 - 08 May 2021
Cited by 2 | Viewed by 692
Abstract
In 2018, Tseng et al. proposed a dual-image reversible embedding method based on the modified Least Significant Bit matching (LSB matching) method. This method improved on the dual-image LSB matching method proposed by Lu et al. In Lu et al.’s scheme, there are [...] Read more.
In 2018, Tseng et al. proposed a dual-image reversible embedding method based on the modified Least Significant Bit matching (LSB matching) method. This method improved on the dual-image LSB matching method proposed by Lu et al. In Lu et al.’s scheme, there are seven situations that cannot be restored and need to be modified. Furthermore, the scheme uses two pixels to conceal four secret bits. The maximum modification of each pixel, in Lu et al.’s scheme, is two. To decrease the modification, Tseng et al. use one pixel to embed two secret bits and allow the maximum modification to decrease from two to one such that the image quality can be improved. This study enhances Tseng et al.’s method by re-encoding the modified rule table based on the probability of each hiding combination. The scheme analyzes the frequency occurrence of each combination and sets the lowest modified codes to the highest frequency case to significantly reduce the amount of modification. Experimental results show that better image quality is obtained using our method under the same amount of hiding payload. Full article
(This article belongs to the Special Issue Entropy Based Data Hiding and Its Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Information Hiding in the DICOM Message Service and Upper Layer Service with Entropy-Based Detection
Authors: Dr. Aleksandra Mileva, Dr. Aleksandar Velinov, Dr. Vezna Dimitrova, Dr. Luca Caviglione and Steffen Wendzel 
Abstract: The Digital Imaging and COmmunication in Medicine (DICOM) standard provides a framework for diagnostically-accurate representation, processing, transfer, storage and display of medical imaging data. Information hiding in DICOM is currently limited to watermatking as well as techniques for concealing images' sensitive data of patients. To improve the overall security of the DICOM standard, we investigate several covert channels that can be created by using a specific transport mechanism -  the DICOM Message Service and Upper Layer Service, which together provide an independence from the underlying network protocols. Moreover, a detection mechanism leveraging entropy-based metrics will be introduced. 

 

Title: Improving Reversible LSB Matching Scheme based on Entropy Re-Encoding Strategy
Authors: Prof. Dr. Tzu Chuen Lu
Affiliation: Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
Abstract: In 2019, Tseng et al. proposed a dual-image reversible embedding method based on the modified Least Significant Bit Matching (LSB matching) method. This method improved the problem of the dual-image LSB matching method of Lu et al.’s scheme. In Lu et al.'s scheme there are seven situations that cannot be restored and need to be modified. In Lu et al.’s scheme, two pixels are used to conceal four secret bits and the maximum modification of each pixel in Lu et al.’s scheme is 2. In order to decrease the modification, Tseng et al. used one pixel to embed two secret bits and let the maximum modification from 2 to 1 such that the image quality can be improved. This study enhances Tseng et al.’s method by re-encoding the modified mapping table based on the entropy of each hiding combination. The scheme analysis the frequency occurrence of each situation and sets the lowest modified codes to the most frequency case to significantly reduce the amount of modification. Experimental results show that better image quality is obtained in our method under the same amount of hiding payload.

 

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