Special Issue "Data Hiding and Its Applications: Digital Watermarking and Steganography"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Prof. Dr. David Megías
Guest Editor
Internet Interdisciplinary Institute (IN3), CYBERCAT-Center for Cybersecurity Research of Catalonia, Universitat Oberta de Catalunya (UOC), Av. Carl Friedrich Gauss, 5, 08860 Castelldefels (Barcelona), Spain
Interests: information security and privacy; multimedia security; watermarking; steganography; fingerprinting; steganalysis; security protocols; privacy-preserving technologies; network security
Special Issues and Collections in MDPI journals
Prof. Dr. Wojciech Mazurczyk
Guest Editor
Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
Interests: information hiding; steganography; network security; traffic measurements; bio-inspired cybersecurity
Prof. Dr. Minoru Kuribayashi
Guest Editor
Graduate School of Natural Science and Technology, Okayama University, 700-8530, Okayama, Japan
Interests: multimedia security; fingerprinting; traitor tracing; signal processing; cryptographic protocol; coding theory; statistical analysis
Special Issues and Collections in MDPI journals

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 relevant in providing multimedia security. Due to the complementary nature of general requirements of these methods, i.e., imperceptibility, robustness, security, and capacity, many data hiding schemes attempt to find optimal performance.

There are many potential applications of data hiding techniques. Copyright protection一via content proof of ownership, owner identification or transaction tracking (fingerprinting), broadcast monitoring, content authentication, including tampering detection or localization一copy control, device control, and legacy enhancement stand out among the applications of digital watermarking. On the other hand, secret communications are the focus of steganography, either for military reasons or for dissidents or for criminal organizations. The military and criminal applications of steganography have led to an increased interest of the academic community in steganalysis, i.e., the techniques used to detect steganographic communications.

The goal of this Special Issue is to focus on the improvement of data hiding algorithms and their different applications. 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.

Prof. Dr. David Megías
Prof. Dr. Wojciech Mazurczyk
Prof. 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1800 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.


Specific Keywords



Digital watermarking

Zero watermarking

Digital fingerprinting

Coverless data hiding

Reversible data hiding and applications

Forensic aspects of data hiding

Embedding capacity/payload

Emerging applications of data hiding in IoT and Big Data

Applications of data hiding


Generic Keywords

Ownership Proof/Copyright Protection

Covert channels


Embedding capacity


Data integrity

Distortion measurement

Transform coding

Information theory

Information entropy

Signal processing

Data segmentation

Published Papers (1 paper)

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Open AccessArticle
Fragile Watermarking of 3D Models in a Transformed Domain
Appl. Sci. 2020, 10(9), 3244; https://doi.org/10.3390/app10093244 - 07 May 2020
This paper presents an algorithm aimed at the integrity protection of 3D models represented as a set of vertices and polygons. The proposed method defines a procedure to perform a fragile watermarking of the vertices’ data, namely 3D coordinates and polygons, introducing a [...] Read more.
This paper presents an algorithm aimed at the integrity protection of 3D models represented as a set of vertices and polygons. The proposed method defines a procedure to perform a fragile watermarking of the vertices’ data, namely 3D coordinates and polygons, introducing a very small error in the vertices’ coordinates. The watermark bit string is embedded into a secret vector space defined by the Karhunen–Loève transform derived from a key image. Experimental results show the good performance of the method and its security. Full article
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