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Entropy
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Information Hiding and Secret Sharing for New Carriers and Their...
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Information Hiding and Secret Sharing for New Carriers and Their Security Evaluation Methods

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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 2408

Special Issue Editors

Dr. Xuehu Yan

E-Mail Website
Guest Editor
The College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.
Interests: secret image sharing; information hiding; AI security; multimedia security; air-gapped
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Li

E-Mail
Guest Editor
Department of Mathematics and Physics, North China Electric Power University, Baoding 071003, China
Interests: secret sharing; secret image sharing; polynomial-based secret image sharing; information hiding; multimedia security; information theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Information hiding and secret sharing are key techniques used to protect private information; they can be applied to copyright protection, access control, etc. Many new carriers have appeared in recent years, such as AI, encrypted multimedia, and barcodes, whose security is crucial. AI is widely developed and used nowadays, and its security has attracted the attention of researchers and engineers. Multimedia sources, such as an image, are encrypted first and then stored in the cloud, thus making encrypted multimedia a new carrier. Barcodes, like QR codes, are widely used, and their security is important for everyone. In addition, security evaluation methods of information protection techniques are important but difficult as well.

In this Special Issue, first we intend to consider information hiding and secret sharing for new carriers, such as deep learning models, encrypted multimedia, and barcodes, in order to protect their security. Second, we intend to consider the security evaluation methods of the above related protection techniques for new carriers, like using information theory or lossy entropy.

Dr. Xuehu Yan
Dr. Peng Li
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 submissions that pass pre-check are 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 2600 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

  • information hiding
  • secret image sharing
  • AI security
  • multimedia security
  • barcode

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Published Papers (2 papers)

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Research

11 pages, 3819 KiB  
Article
Improved CNN Prediction Based Reversible Data Hiding for Images
by Yingqiang Qiu, Wanli Peng and Xiaodan Lin
Entropy 2025, 27(2), 159; https://doi.org/10.3390/e27020159 - 3 Feb 2025
Cited by 1 | Viewed by 827
Abstract
This paper proposes a reversible data hiding (RDH) scheme for images with an improved convolutional neural network (CNN) predictor (ICNNP) that consists of three modules for feature extraction, pixel prediction, and complexity prediction, respectively. Due to predicting the complexity of each pixel with [...] Read more.
This paper proposes a reversible data hiding (RDH) scheme for images with an improved convolutional neural network (CNN) predictor (ICNNP) that consists of three modules for feature extraction, pixel prediction, and complexity prediction, respectively. Due to predicting the complexity of each pixel with the ICNNP during the embedding process, the proposed scheme can achieve superior performance compared to a CNNP-based scheme. Specifically, an input image is first split into two sub-images, i.e., a “Circle” sub-image and a “Square” sub-image. Meanwhile, each sub-image is applied to predict another one with the ICNNP. Then, the prediction errors of pixels are sorted based on the predicted pixel complexities. In light of this, some sorted prediction errors with less complexity are selected to be efficiently applied for low-distortion data embedding with a traditional histogram-shifting technique. Experimental results show that the proposed ICNNP can achieve better rate-distortion performance than the CNNP, demonstrating its effectiveness. Full article
(This article belongs to the Special Issue Information Hiding and Secret Sharing for New Carriers and Their Security Evaluation Methods)
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15 pages, 1173 KiB  
Article
Image Forensics in the Encrypted Domain
by Yongqiang Yu, Yuliang Lu, Longlong Li, Feng Chen and Xuehu Yan
Entropy 2024, 26(11), 900; https://doi.org/10.3390/e26110900 - 24 Oct 2024
Viewed by 1058
Abstract
Encryption techniques used by forgers have thrown out a big possible challenge to forensics. Most traditional forensic tools will fail to detect the forged multimedia, which has been encrypted. Thus, image forensics in the encrypted domain (IFED) is significant. This paper presents the [...] Read more.
Encryption techniques used by forgers have thrown out a big possible challenge to forensics. Most traditional forensic tools will fail to detect the forged multimedia, which has been encrypted. Thus, image forensics in the encrypted domain (IFED) is significant. This paper presents the first introduction of IFED, encompassing its problem description, formal definition, and evaluation metrics. The focus then turns to the challenge of detecting copy–move alterations in the encrypted domain using the classic permutation encryption technique. To tackle this challenge, we introduce and develop a lightweight enhanced forensic network (LEFN) based on deep learning to facilitate automatic IFED. Extensive experiments and analyses were conducted to comprehensively validate the proposed scheme. Full article
(This article belongs to the Special Issue Information Hiding and Secret Sharing for New Carriers and Their Security Evaluation Methods)
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