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Mathematics
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Data Hiding, Steganography and Its Application
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Data Hiding, Steganography and Its Application

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3468

Special Issue Editors

Prof. Dr. Cheonshik Kim

E-Mail Website
Guest Editor
Department of Computer Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: data hiding; steganography; watermarking; multimedia security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ki-Hyun Jung

E-Mail Website
Guest Editor
Department of Software Convergence, Andong National University, Andong 36729, Republic of Korea
Interests: cryptography; VLSI; authentication technologies; network security and ubiquitous computing security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for a Special Issue on «Data Hiding and Steganography and Its Application» to be published in MDPI. Data hiding and steganography have been widely used in various fields, including information security, digital forensics, and copyright protection. In recent years, with the rapid growth of digital media, there has been an increasing demand for efficient and effective techniques for data hiding and steganography. One of the newest issues in data hiding and steganography is the use of deep learning and neural networks to develop more sophisticated hiding and detection techniques. Another emerging issue is the use of data hiding and steganography in the context of cybersecurity and cybercrime. In addition to this main topic, we also welcome papers in various fields that apply digital images, deep learning, and the neural network.

This Special Issue aims to bring together researchers and practitioners to discuss the latest developments in data hiding and steganography, as well as their applications. Topics of interest for this Special Issue include, but are not limited to:

  • Steganography and steganalysis;
  • Watermarking and digital rights management;
  • Cryptography and security in data hiding;
  • Steganography in social media;
  • Information hiding in multimedia and biometrics;
  • Applications of data hiding and steganography in various fields;
  • Applications of image processing in various fields.

Prof. Dr. Cheonshik Kim
Prof. Dr. Ki-Hyun Jung
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. Mathematics 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 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

  • steganography
  • steganalysis
  • watermarking
  • data hiding
  • multimedia
  • social nnetwork
  • image processing

Published Papers (5 papers)

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Research

40 pages, 23599 KiB  
Article
Bio-Inspired Watermarking Method for Authentication of Fundus Images in Computer-Aided Diagnosis of Retinopathy
by Ernesto Moya-Albor, Sandra L. Gomez-Coronel, Jorge Brieva and Alberto Lopez-Figueroa
Mathematics 2024, 12(5), 734; https://doi.org/10.3390/math12050734 - 29 Feb 2024
Viewed by 522
Abstract
Nowadays, medical imaging has become an indispensable tool for the diagnosis of some pathologies and as a health prevention instrument. In addition, medical images are transmitted over all types of computer networks, many of them insecure or susceptible to intervention, making sensitive patient [...] Read more.
Nowadays, medical imaging has become an indispensable tool for the diagnosis of some pathologies and as a health prevention instrument. In addition, medical images are transmitted over all types of computer networks, many of them insecure or susceptible to intervention, making sensitive patient information vulnerable. Thus, image watermarking is a popular approach to embed copyright protection, Electronic Patient Information (EPR), institution information, or other digital image into medical images. However, in the medical field, the watermark must preserve the quality of the image for diagnosis purposes. In addition, the inserted watermark must be robust both to intentional and unintentional attacks, which try to delete or weaken it. This work presents a bio-inspired watermarking algorithm applied to retinal fundus images used in computer-aided retinopathy diagnosis. The proposed system uses the Steered Hermite Transform (SHT), an image model inspired by the Human Vision System (HVS), as a spread spectrum watermarking technique, by leveraging its bio-inspired nature to give imperceptibility to the watermark. In addition, the Singular Value Decomposition (SVD) is used to incorporate the robustness of the watermark against attacks. Moreover, the watermark is embedded into the RGB fundus images through the blood vessel patterns extracted by the SHT and using the luma band of Y’CbCr color model. Also, the watermark was encrypted using the Jigsaw Transform (JST) to incorporate an extra level of security. The proposed approach was tested using the image public dataset MESSIDOR-2, which contains 1748 8-bit color images of different sizes and presenting different Diabetic Retinopathy (DR). Thus, on the one hand, in the experiments we evaluate the proposed bio-inspired watermarking method over the entire MESSIDOR-2 dataset, showing that the embedding process does not affect the quality of the fundus images and the extracted watermark, by obtaining average Peak Signal-to-Noise Ratio (PSNR) values higher to 53 dB for the watermarked images and average PSNR values higher to 32 dB to the extracted watermark for the entire dataset. Also, we tested the method against image processing and geometric attacks successfully extracting the watermarking. A comparison of the proposed method against state-of-the-art was performed, obtaining competitive results. On the other hand, we classified the DR grade of the fundus image dataset using four trained deep learning models (VGG16, ResNet50, InceptionV3, and YOLOv8) to evaluate the inference results using the originals and marked images. Thus, the results show that DR grading remains both in the non-marked and marked images. Full article
(This article belongs to the Special Issue Data Hiding, Steganography and Its Application)
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15 pages, 3322 KiB  
Article
Pooled Steganalysis via Model Discrepancy
by Jiang Yu, Jing Zhang and Fengyong Li
Mathematics 2024, 12(4), 552; https://doi.org/10.3390/math12040552 - 11 Feb 2024
Viewed by 475
Abstract
Pooled steganalysis aims to discover the guilty actor(s) among multiple normal actor(s). Existing techniques mainly rely on the high-dimension and time-consuming features. Moreover, the minor feature distance between cover and stego is detrimental to pooled steganalysis. To overcome these issues, this paper focuses [...] Read more.
Pooled steganalysis aims to discover the guilty actor(s) among multiple normal actor(s). Existing techniques mainly rely on the high-dimension and time-consuming features. Moreover, the minor feature distance between cover and stego is detrimental to pooled steganalysis. To overcome these issues, this paper focuses on the discrepancy of the statistical characteristics of transmitted multiple images and designs a model-based effective pooled steganalysis strategy. Facing the public and monitored channel, without using the feature extractions, pooled steganalysis collects a set of images transmitted by a suspicious actor and use the corresponding distortion values as the statistic representation of the selected image set. Specifically, the normalized distortion of the suspicious image set generated via normal/guilty actor(s) is modelled as a normal distribution, and we apply maximum likelihood estimation (MLE) to estimate the parameter (cluster center) of the distribution by which we can represent the defined model. Considering the tremendous distortion difference between normal and stego image sets, we can deduce that the constructed model can effectively discover and reveal the existence of abnormal behavior of guilty actors. To show the discrepancy of different models, employing the logistic function and likelihood ratio test (LRT), we construct a new detector by which the ratio of cluster centers is turned into a probability. Depending on the generated probability and an optimal threshold, we make a judgment on whether the dubious actor is normal or guilty. Extensive experiments demonstrate that, compared to existing pooled steganalysis techniques, the proposed scheme exhibits great detection performance on the guilty actor(s) with lower complexity. Full article
(This article belongs to the Special Issue Data Hiding, Steganography and Its Application)
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13 pages, 4835 KiB  
Article
A Matrix Coding-Oriented Reversible Data Hiding Scheme Using Dual Digital Images
by Jui-Chuan Liu, Ching-Chun Chang, Yijie Lin, Chin-Chen Chang and Ji-Hwei Horng
Mathematics 2024, 12(1), 86; https://doi.org/10.3390/math12010086 - 26 Dec 2023
Cited by 1 | Viewed by 629
Abstract
With the development of Internet technology, information security and data protection have become particularly important. Reversible data hiding is an effective technique for data integrity and privacy protection, and secret image sharing is a distinct research field within reversible data hiding. Due to [...] Read more.
With the development of Internet technology, information security and data protection have become particularly important. Reversible data hiding is an effective technique for data integrity and privacy protection, and secret image sharing is a distinct research field within reversible data hiding. Due to the ability of sharing secret information between two receivers and the larger embedding capacity compared to the traditional reversible data hiding scheme, dual digital images have also attracted extensive research in the past decade. In this paper, we propose a reversible data hiding scheme based on matrix coding using dual digital images. By modifying the bits in the pixels, we can conceal three bits of the secret message in two pixels. In other words, the embedding rate reaches 1.5 bits per pixel (bpp). The experimental results demonstrate that our method has a significantly larger embedding capacity of 786,432 bits compared to previous similar methods while still maintaining acceptable image quality defined by a peak signal-to-noise ratio (PSNR) greater than 30 dB. The proposed scheme is suitable for applications required to pass a large amount of data but with minor security of image quality to be visually acceptable. Full article
(This article belongs to the Special Issue Data Hiding, Steganography and Its Application)
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16 pages, 5077 KiB  
Article
Reversible Data Hiding in Encrypted Images Based on Two-Round Image Interpolation
by Qing Zhang and Kaimeng Chen
Mathematics 2024, 12(1), 32; https://doi.org/10.3390/math12010032 - 22 Dec 2023
Cited by 1 | Viewed by 561
Abstract
The data embedding of vacating room after encryption reversible data hiding in encrypted images (VRAE RDHEI) is performed on an encrypted image without redundancy and spatial correlation. Data extraction and image recovery rely on a range of unique mechanisms that utilize spatial correlation [...] Read more.
The data embedding of vacating room after encryption reversible data hiding in encrypted images (VRAE RDHEI) is performed on an encrypted image without redundancy and spatial correlation. Data extraction and image recovery rely on a range of unique mechanisms that utilize spatial correlation in the decrypted domain. Of these mechanisms, pixel prediction is among the most frequently used, directly affecting the capacity and fidelity. In this paper, we propose a novel method that uses a two-round interpolation mechanism to enhance pixel prediction precision while preserving a large number of carrier pixels. In the proposed method, the content owner uses a stream cipher to encrypt the image as a carrier. The data hider flips specific LSBs of the encrypted image for data embedding. On the receiver side, the process of data extraction and image recovery is divided into two stages. In each stage, based on the varying distributions of the original or recovered pixels with the carrier pixels, the corresponding pixel interpolation schemes are used to accurately predict the pixels for data extraction and image recovery. The results demonstrate that the proposed method can efficiently improve the capacity and fidelity with full reversibility compared to existing VRAE RDHEI methods. Full article
(This article belongs to the Special Issue Data Hiding, Steganography and Its Application)
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12 pages, 3074 KiB  
Article
High-Pass-Kernel-Driven Content-Adaptive Image Steganalysis Using Deep Learning
by Saurabh Agarwal, Hyenki Kim and Ki-Hyun Jung
Mathematics 2023, 11(20), 4322; https://doi.org/10.3390/math11204322 - 17 Oct 2023
Viewed by 754
Abstract
Digital images cannot be excluded as part of a popular choice of information representation. Covert information can be easily hidden using images. Several schemes are available to hide covert information and are known as steganography schemes. Steganalysis schemes are applied on stego-images to [...] Read more.
Digital images cannot be excluded as part of a popular choice of information representation. Covert information can be easily hidden using images. Several schemes are available to hide covert information and are known as steganography schemes. Steganalysis schemes are applied on stego-images to assess the strength of steganography schemes. In this paper, a new steganalysis scheme is presented to detect stego-images. Predefined kernels guide the set of a conventional convolutional layer, and the tight cohesion provides completely guided training. The learning rate of convolutional layers with predefined kernels is higher than the global learning rate. The higher learning rate of the convolutional layers with predefined kernels assures adaptability according to network training, while still maintaining the basic attributes of high-pass kernels. The Leaky ReLU layer is exploited against the ReLU layer to boost the detection performance. Transfer learning is applied to enhance detection performance. The deep network weights are initialized using the weights of the trained network from high-payload stego-images. The strength of the proposed scheme is verified on the HILL, Mi-POD, S-UNIWARD, and WOW content-adaptive steganography schemes. The results are overwhelming and better than the existing steganalysis schemes. Full article
(This article belongs to the Special Issue Data Hiding, Steganography and Its Application)
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