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Special Issue "Entropy Based Data Hiding"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: 31 May 2019

Special Issue Editors

Guest Editor
Dr. David Megías Jiménez

Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Parc Mediterrani de la Tecnologia (edifici B3), Av. Carl Friedrich Gauss, 5, 08860, Castelldefels, Spain
Website | E-Mail
Interests: Information security and privacy; copyright protection; multimedia content (digital image, audio and video); watermarking; fingerprinting; steganography; signal processing
Guest Editor
Dr. Minoru Kuribayashi

Graduate School of Natural Science and Technology, Okayama University, Okayama 700-8530, Japan.
Website | E-Mail
Interests: Information security, watermarking, fingerprinting, collusion secure code, cryptographic protocol, coding theory

Special Issue Information

Dear Colleagues,

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of an increased dissemination and distribution of multimedia content (text, audio, video, etc.) over the Internet, data hiding methods, such as digital watermarking and steganography, are becoming more and more important in providing multimedia security. Due to the complimentary nature of general requirements of these methods, i.e., imperceptibility, robustness, security and capacity, many data hiding schemes attempt to find optimal performance by applying concepts of information theory, such as entropy. Entropy has been used extensively to support data hiding algorithms. Examples include the use of information entropy as a “masking effect” of Just Noticeable Difference (JND) model in digital watermarking systems to obtain a better trade-off between imperceptibility and robustness; as a measure to evaluate the complexity of cover object to achieve Bit Error Rate (BER) constraint in steganographic schemes; as a criterion to determine the embedding positions in the cover data so as to cause minimal perceptual distortion; and as a measure to evaluate information leakage in the embedding process.

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.

Dr. David Megías Jiménez
Dr. Minoru Kuribayashi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Specific Keywords Generic Keywords
Entropy-based audio/video/image Steganalysis Steganalysis
Entropy-based audio/video/image Steganography Steganography
Entropy-based audio/video/image Watermarking Digital Watermarking
Coverless Data Hiding Digital Fingerprinting
Reversible Data Hiding and Applications Entropy
Forensic Aspects of Data Hiding Traitor-Tracing
Blind Detection/Extraction Ownership Proof/Copyright Protection
Visual Cryptography Transform Coding
Embedding Capacity Data Segmentation
Signal Processing Embedding Capacity
Emerging Applications of Data Hiding in IoT and Big Data Extraction/Detection
  Data Integrity
  Distortion Measurement
  Stego signal/Payload

Published Papers (2 papers)

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Research

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Open AccessArticle
An Adaptive and Secure Holographic Image Watermarking Scheme
Entropy 2019, 21(5), 460; https://doi.org/10.3390/e21050460
Received: 22 March 2019 / Revised: 27 April 2019 / Accepted: 28 April 2019 / Published: 2 May 2019
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Abstract
A novel adaptive secure holographic image watermarking method in the sharp frequency localized contourlet transform (SFLCT) domain is presented. Based upon the sine logistic modulation map and the logistic map, we develop an encrypted binary computer-generated hologram technique to fabricate a hologram of [...] Read more.
A novel adaptive secure holographic image watermarking method in the sharp frequency localized contourlet transform (SFLCT) domain is presented. Based upon the sine logistic modulation map and the logistic map, we develop an encrypted binary computer-generated hologram technique to fabricate a hologram of a watermark first. Owing to the enormous key space of the encrypted hologram, the security of the image watermarking system is increased. Then the hologram watermark is embedded into the SFLCT coefficients with Schur decomposition. To obtain better imperceptibility and robustness, the entropy and the edge entropy are utilized to select the suitable watermark embedding positions adaptively. Compared with other watermarking schemes, the suggested method provides a better performance with respect to both imperceptibility and robustness. Experiments show that our watermarking scheme for images is not only is secure and invisible, but also has a stronger robustness against different kinds of attack. Full article
(This article belongs to the Special Issue Entropy Based Data Hiding)
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Review

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Open AccessReview
Modern Text Hiding, Text Steganalysis, and Applications: A Comparative Analysis
Entropy 2019, 21(4), 355; https://doi.org/10.3390/e21040355
Received: 9 March 2019 / Revised: 26 March 2019 / Accepted: 27 March 2019 / Published: 1 April 2019
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Abstract
Modern text hiding is an intelligent programming technique which embeds a secret message/watermark into a cover text message/file in a hidden way to protect confidential information. Recently, text hiding in the form of watermarking and steganography has found broad applications in, for instance, [...] Read more.
Modern text hiding is an intelligent programming technique which embeds a secret message/watermark into a cover text message/file in a hidden way to protect confidential information. Recently, text hiding in the form of watermarking and steganography has found broad applications in, for instance, covert communication, copyright protection, content authentication, etc. In contrast to text hiding, text steganalysis is the process and science of identifying whether a given carrier text file/message has hidden information in it, and, if possible, extracting/detecting the embedded hidden information. This paper presents an overview of state of the art of the text hiding area, and provides a comparative analysis of recent techniques, especially those focused on marking structural characteristics of digital text message/file to hide secret bits. Also, we discuss different types of attacks and their effects to highlight the pros and cons of the recently introduced approaches. Finally, we recommend some directions and guidelines for future works. Full article
(This article belongs to the Special Issue Entropy Based Data Hiding)
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