Special Issue "Entropy Based Data Hiding"

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

Deadline for manuscript submissions: closed (31 August 2019).

Special Issue Editors

Dr. David Megías
Website
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. Minoru Kuribayashi
Website
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 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

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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. 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 (8 papers)

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Research

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Open AccessArticle
On a Key-Based Secured Audio Data-Hiding Scheme Robust to Volumetric Attack with Entropy-Based Embedding
Entropy 2019, 21(10), 996; https://doi.org/10.3390/e21100996 - 12 Oct 2019
Abstract
In the data-hiding field, it is mandatory that proposed schemes are key-secured as required by the Kerckhoff’s principle. Moreover, perceptual transparency must be guaranteed. On the other hand, volumetric attack is of special interest in audio data-hiding systems. This study proposes a data-hiding [...] Read more.
In the data-hiding field, it is mandatory that proposed schemes are key-secured as required by the Kerckhoff’s principle. Moreover, perceptual transparency must be guaranteed. On the other hand, volumetric attack is of special interest in audio data-hiding systems. This study proposes a data-hiding scheme for audio signals, which is both key-based secured and highly perceptually transparent and, thus, robust to the volumetric attack. A modification to a state-of-the-art data-hiding algorithm is proposed to achieve key-based security. Embedding is carried out in the integer discrete cosine transform (DCT) domain; selected samples for embedding are determined by the entropy of the Integer DCT coefficients. Of the two key-based improvements proposed, the multiplicative strategy gives better results, guaranteeing the worst bit error rate when an incorrect key is used. Additionally, the perceptual transparency of the proposed scheme is higher, compared to the state-of-the-art schemes using similar embedding strategies. Full article
(This article belongs to the Special Issue Entropy Based Data Hiding)
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Open AccessArticle
Lossless Contrast Enhancement of Color Images with Reversible Data Hiding
Entropy 2019, 21(9), 910; https://doi.org/10.3390/e21090910 - 18 Sep 2019
Abstract
Recently, lossless contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version by maintaining information entropy in it. As most of the lossless CE methods are proposed for grayscale images, artifacts are probably introduced after [...] Read more.
Recently, lossless contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version by maintaining information entropy in it. As most of the lossless CE methods are proposed for grayscale images, artifacts are probably introduced after directly applying them to color images. For instance, color distortions may be caused after CE is separately conducted in each channel of the RGB (red, green, and blue) model. To cope with this issue, a new scheme is proposed based on the HSV (hue, saturation, and value) color model. Specifically, both hue and saturation components are kept unchanged while only the value component is modified. More precisely, the ratios between the RGB components are maintained while a reversible data hiding method is applied to the value component to achieve CE effects. The experimental results clearly show CE effects obtained with the proposed scheme, while the original color images can be perfectly recovered. Several metrics including image entropy were adopted to measure the changes made in CE procedure, while the performances were compared with those of one existing scheme. The evaluation results demonstrate that better image quality and increased information entropy can be simultaneously achieved with our proposed scheme. Full article
(This article belongs to the Special Issue Entropy Based Data Hiding)
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Open AccessFeature PaperArticle
Entropy-Based Semi-Fragile Watermarking of Remote Sensing Images in the Wavelet Domain
Entropy 2019, 21(9), 847; https://doi.org/10.3390/e21090847 - 30 Aug 2019
Cited by 1
Abstract
This article presents a semi-fragile image tampering detection method for multi-band images. In the proposed scheme, a mark is embedded into remote sensing images, which have multiple frequential values for each pixel, applying tree-structured vector quantization. The mark is not embedded into each [...] Read more.
This article presents a semi-fragile image tampering detection method for multi-band images. In the proposed scheme, a mark is embedded into remote sensing images, which have multiple frequential values for each pixel, applying tree-structured vector quantization. The mark is not embedded into each frequency band separately, but all the spectral values (known as signature) are used. The mark is embedded in the signature as a means to detect if the original image has been forged. The image is partitioned into three-dimensional blocks with varying sizes. The size of these blocks and the embedded mark is determined by the entropy of each region. The image blocks contain areas that have similar pixel values and represent smooth regions in multispectral or hyperspectral images. Each block is first transformed using the discrete wavelet transform. Then, a tree-structured vector quantizer (TSVQ) is constructed from the low-frequency region of each block. An iterative algorithm is applied to the generated trees until the resulting tree fulfils a requisite criterion. More precisely, the TSVQ tree that matches a particular value of entropy and provides a near-optimal value according to Shannon’s rate-distortion function is selected. The proposed method is shown to be able to preserve the embedded mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their positions in the whole image. Experimental results show how the scheme can be applied to detect forgery attacks, and JPEG2000 compression of the images can be applied without removing the authentication mark. The scheme is also compared to other works in the literature. Full article
(This article belongs to the Special Issue Entropy Based Data Hiding)
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Open AccessArticle
Reversible Data Hiding in JPEG Images Using Quantized DC
Entropy 2019, 21(9), 835; https://doi.org/10.3390/e21090835 - 26 Aug 2019
Cited by 1
Abstract
Reversible data hiding in JPEG images has become an important topic due to the prevalence and overwhelming support of the JPEG image format these days. Much of the existing work focuses on embedding using AC (quantized alternating current coefficients) to maximize the embedding [...] Read more.
Reversible data hiding in JPEG images has become an important topic due to the prevalence and overwhelming support of the JPEG image format these days. Much of the existing work focuses on embedding using AC (quantized alternating current coefficients) to maximize the embedding capacity while minimizing the distortion and the file size increase. Traditionally, DC (quantized direct current coefficients) are not used for embedding, due to the assumption that the embedding in DCs cause more distortion than embedding in ACs. However, for data analytic which extracts fine details as a feature, distortion in ACs is not acceptable, because they represent the fine details of the image. In this paper, we propose a novel reversible data hiding method which efficiently embeds in the DC. The propose method uses a novel DC prediction method to decrease the entropy of the prediction error histogram. The embedded image has higher PSNR, embedding capacity, and smaller file size increase. Furthermore, proposed method preserves all the fine details of the image. Full article
(This article belongs to the Special Issue Entropy Based Data Hiding)
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Open AccessArticle
Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors
Entropy 2019, 21(8), 748; https://doi.org/10.3390/e21080748 - 30 Jul 2019
Abstract
In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two [...] Read more.
In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain. The chaotic system is extremely robust and consists of a strong chaotic generator and a 2-D Cat map. Its main role is to secure the content of a message in case a message is detected. Secondly, three blind steganalysis methods, based on multi-resolution wavelet decomposition, are used to detect whether an embedded message is hidden in the tested image (stego image) or not (cover image). The steganalysis approach is based on the hypothesis that message-embedding schemes leave statistical evidence or structure in images that can be exploited for detection. The simulation results show that the Support Vector Machine (SVM) classifier and the Fisher Linear Discriminant (FLD) cannot distinguish between cover and stego images if the message size is smaller than 20% in the EEALSBMR steganographic method and if the message size is smaller than 15% in the EDCT steganographic method. However, SVM and FLD can distinguish between cover and stego images with reasonable accuracy in the EDWT steganographic method, irrespective of the message size. Full article
(This article belongs to the Special Issue Entropy Based Data Hiding)
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Open AccessArticle
Reversible Data Hiding Algorithm in Fully Homomorphic Encrypted Domain
Entropy 2019, 21(7), 625; https://doi.org/10.3390/e21070625 - 26 Jun 2019
Cited by 3
Abstract
This paper proposes a reversible data hiding scheme by exploiting the DGHV fully homomorphic encryption, and analyzes the feasibility of the scheme for data hiding from the perspective of information entropy. In the proposed algorithm, additional data can be embedded directly into a [...] Read more.
This paper proposes a reversible data hiding scheme by exploiting the DGHV fully homomorphic encryption, and analyzes the feasibility of the scheme for data hiding from the perspective of information entropy. In the proposed algorithm, additional data can be embedded directly into a DGHV fully homomorphic encrypted image without any preprocessing. On the sending side, by using two encrypted pixels as a group, a data hider can get the difference of two pixels in a group. Additional data can be embedded into the encrypted image by shifting the histogram of the differences with the fully homomorphic property. On the receiver side, a legal user can extract the additional data by getting the difference histogram, and the original image can be restored by using modular arithmetic. Besides, the additional data can be extracted after decryption while the original image can be restored. Compared with the previous two typical algorithms, the proposed scheme can effectively avoid preprocessing operations before encryption and can successfully embed and extract additional data in the encrypted domain. The extensive testing results on the standard images have certified the effectiveness of the proposed scheme. Full article
(This article belongs to the Special Issue Entropy Based Data Hiding)
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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 - 02 May 2019
Cited by 2
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 - 01 Apr 2019
Cited by 6
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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