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16 pages, 3489 KiB

Open AccessArticle

On a Key-Based Secured Audio Data-Hiding Scheme Robust to Volumetric Attack with Entropy-Based Embedding

by Jose Juan Garcia-Hernandez

Entropy 2019, 21(10), 996; https://doi.org/10.3390/e21100996 - 12 Oct 2019

Cited by 2 | Viewed by 2174

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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14 pages, 2444 KiB

Open AccessArticle

Lossless Contrast Enhancement of Color Images with Reversible Data Hiding

by Hao-Tian Wu, Yue Wu, Zhihao Guan and Yiu-ming Cheung

Entropy 2019, 21(9), 910; https://doi.org/10.3390/e21090910 - 18 Sep 2019

Cited by 13 | Viewed by 3320

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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21 pages, 5166 KiB

Open AccessFeature PaperArticle

Entropy-Based Semi-Fragile Watermarking of Remote Sensing Images in the Wavelet Domain

by Jordi Serra-Ruiz, Amna Qureshi and David Megías

Entropy 2019, 21(9), 847; https://doi.org/10.3390/e21090847 - 30 Aug 2019

Cited by 12 | Viewed by 3207

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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14 pages, 1674 KiB

Open AccessArticle

Reversible Data Hiding in JPEG Images Using Quantized DC

by Suah Kim, Fangjun Huang and Hyoung Joong Kim

Entropy 2019, 21(9), 835; https://doi.org/10.3390/e21090835 - 26 Aug 2019

Cited by 13 | Viewed by 4049

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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36 pages, 3919 KiB

Open AccessArticle

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors

by Dalia Battikh, Safwan El Assad, Thang Manh Hoang, Bassem Bakhache, Olivier Deforges and Mohamad Khalil

Entropy 2019, 21(8), 748; https://doi.org/10.3390/e21080748 - 30 Jul 2019

Cited by 6 | Viewed by 3502

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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14 pages, 1315 KiB

Open AccessArticle

Reversible Data Hiding Algorithm in Fully Homomorphic Encrypted Domain

by Jingxuan Li, Xingyuan Liang, Ceyu Dai and Shijun Xiang

Entropy 2019, 21(7), 625; https://doi.org/10.3390/e21070625 - 26 Jun 2019

Cited by 9 | Viewed by 3821

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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14 pages, 8212 KiB

Open AccessArticle

An Adaptive and Secure Holographic Image Watermarking Scheme

by Chuying Yu, Xiaowei Li, Xinan Chen and Jianzhong Li

Entropy 2019, 21(5), 460; https://doi.org/10.3390/e21050460 - 2 May 2019

Cited by 12 | Viewed by 3060

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

Jump to: Research

31 pages, 4757 KiB

Open AccessReview

Modern Text Hiding, Text Steganalysis, and Applications: A Comparative Analysis

by Milad Taleby Ahvanooey, Qianmu Li, Jun Hou, Ahmed Raza Rajput and Yini Chen

Entropy 2019, 21(4), 355; https://doi.org/10.3390/e21040355 - 1 Apr 2019

Cited by 46 | Viewed by 10687

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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Graphical abstract

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

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