Special Issue "Computing Methods in Steganography and Multimedia Security"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational Mathematics".

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editors

Prof. James C.N. Yang
E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan
Interests: information security; cryptography; coding theory
Special Issues and Collections in MDPI journals
Prof. Cheonshik Kim
E-Mail Website
Guest Editor
Department of Computer Science & Engineering, Sejong University, Seoul, Korea
Interests: data hiding; steganography; watermarking; multimedia security
Prof. Stelvio Cimato
E-Mail Website
Guest Editor
Department of Computer Science, Università degli studi di Milano, Milan, Italy
Interests: data and network security; cryptographic protocols and applications

Special Issue Information

Dear Colleagues,

With the progress of digital multimedia (image, audio, and video) techniques and the popularity of transmitting digital multimedia over the Internet, protecting multimedia communication in a secure way, without obstructing access from authorized parties, has become a critical issue. In addition, techniques such as steganography have been used in attempts to enhance multimedia security. Steganography is the science of communicating secret information in a hidden manner, and it usually uses multimedia data as vehicles for secret communication, so that the multimedia data can be shared and distributed on the Internet.

The above has aroused the interest of researchers on how to propose and design new effective approaches for steganography and multimedia security. Many steganographic methods or other protecting technologies have been proposed on multimedia. Therefore, to achieve a desirable performance, it is required to specially design efficient and effective computing methods for steganography and multimedia security. The aim of this Special Issue is to collect papers that apply innovative computing methods for steganography and multimedia security. We solicit original research and survey papers addressing (but not limited to) the topics listed below:

Topics:

  • Computation methods in image/video steganography;
  • Computation methods in image/video steganalysis;
  • Computation methods in multimedia content protection;
  • Computation methods in multimedia encryption and authentication;
  • Computation methods in multimedia privacy and analysis;
  • Computation methods in multimedia forensics;
  • AI and intelligent multimedia computing.

Prof. James C.N. Yang
Prof. Cheonshik Kim
Prof. Stelvio Cimato
Guest Editors

Manuscript Submission Information

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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. Mathematics 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 1200 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.

Published Papers (4 papers)

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Research

Open AccessArticle
Secret Image Sharing with Dealer-Participatory and Non-Dealer-Participatory Mutual Shadow Authentication Capabilities
Mathematics 2020, 8(2), 234; https://doi.org/10.3390/math8020234 - 12 Feb 2020
Abstract
A (k,n) threshold secret image sharing (SIS) method is proposed to divide a secret image into n shadows. The beauty of this scheme is that one can only reconstruct a secret image with k or more than k shadows, [...] Read more.
A ( k , n ) threshold secret image sharing (SIS) method is proposed to divide a secret image into n shadows. The beauty of this scheme is that one can only reconstruct a secret image with k or more than k shadows, but one cannot obtain any information about the secret from fewer than k shadows. In the ( k , n ) threshold SIS, shadow authentication means the detection and location of manipulated shadows. Traditional shadow authentication schemes require additional bits for authentication; need much information to be public; or need to put each shadow into a host image, utilizing the information hiding technique, which makes the generation, recovery and authentication complexity higher. Besides, most existing schemes work when a dealer participates in recovery. Our contribution is that we propose a SIS method for a ( k , n ) threshold with dealer-participatory and non-dealer-participatory mutual shadow authentication capabilities which integrates polynomial-based SIS and visual secret sharing (VSS) through using the result of VSS to "guide" the polynomial-based SIS by a screening operation. In our scheme, when an authentication image is public, all involved actors (participants and dealer) can mutually authenticate each other by exchange the lowest level plane instead of the whole shadow. Our scheme is suitable for the case with and without a dealer participate recovery. In addition, the proposed scheme has characteristics of low generation and authentication complexity, no pixel expansion, 100% detection rate and lossless recovery. Full article
(This article belongs to the Special Issue Computing Methods in Steganography and Multimedia Security)
Open AccessArticle
SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security
Mathematics 2020, 8(1), 93; https://doi.org/10.3390/math8010093 - 07 Jan 2020
Abstract
Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box [...] Read more.
Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box with a higher average intersection ratio is obtained by combining niche technology on the basis of the k-means algorithm. An upsampling scale is added to form a face network structure that is suitable for detecting dense small scale faces. The number of prediction boxes is five times more than the YOLOV3 network. To further improve the detection performance, we adopt the SENet structure to enhance the global receptive field of the network. The experimental results on the WIDERFACEdataset show that the IYOLOV3 network embedded in the SENet structure can significantly improve the detection accuracy of dense small scale faces. Full article
(This article belongs to the Special Issue Computing Methods in Steganography and Multimedia Security)
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Open AccessArticle
Separable Reversible Data Hiding in Encrypted Image Based on Two-Dimensional Permutation and Exploiting Modification Direction
Mathematics 2019, 7(10), 976; https://doi.org/10.3390/math7100976 - 15 Oct 2019
Abstract
In this paper, we propose a separable reversible data hiding method in encrypted image (RDHEI) based on two-dimensional permutation and exploiting modification direction (EMD). The content owner uses two-dimensional permutation to encrypt original image through encryption key, which provides confidentiality for the original [...] Read more.
In this paper, we propose a separable reversible data hiding method in encrypted image (RDHEI) based on two-dimensional permutation and exploiting modification direction (EMD). The content owner uses two-dimensional permutation to encrypt original image through encryption key, which provides confidentiality for the original image. Then the data hider divides the encrypted image into a series of non-overlapping blocks and constructs histogram of adjacent encrypted pixel errors. Secret bits are embedded into a series of peak points of the histogram through EMD. Direct decryption, data extraction and image recovery can be performed separately by the receiver according to the availability of encryption key and data-hiding key. Different from some state-of-the-art RDHEI methods, visual quality of the directly decrypted image can be further improved by the receiver holding the encryption key. Experimental results demonstrate that the proposed method outperforms some state-of-the-art methods in embedding capacity and visual quality. Full article
(This article belongs to the Special Issue Computing Methods in Steganography and Multimedia Security)
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Open AccessArticle
A Novel Image Tamper Detection and Self-Recovery Algorithm Based on Watermarking and Chaotic System
Mathematics 2019, 7(10), 955; https://doi.org/10.3390/math7100955 - 12 Oct 2019
Abstract
With the development of image editing software techniques, the content integrity and authenticity of original digital images become more and more important in digital content security. A novel image tampering detection and recovery algorithm based on digital watermarking technology and a chaotic system [...] Read more.
With the development of image editing software techniques, the content integrity and authenticity of original digital images become more and more important in digital content security. A novel image tampering detection and recovery algorithm based on digital watermarking technology and a chaotic system is proposed, and it can effectively locate the tampering region and achieve the approximate recovery of the original image by using the hidden information. The pseudo-random cyclic chain is realized by the chaotic system to construct the mapping relationship between the image subblocks. It can effectively guarantee the randomness of the positional relationship between the hidden information and the original image block for the better ergodicity of the pseudo-random chain. The recovery value optimization algorithm can represent image information better. In addition to the traditional Level-1 recovery, a weight adaptive algorithm is designed to distinguish the original block from the primary recovery block, allowing 3 × 3 neighbor block recovery to achieve better results. The experimental results show that the hierarchical tamper detection algorithm makes tamper detection have higher precision. When facing collage attacks and large general tampering, it will have higher recovery image quality and better resistance performance. Full article
(This article belongs to the Special Issue Computing Methods in Steganography and Multimedia Security)
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