Reversible Data Hiding Algorithm Research Articles

Adaptive feature selection and optimized multiple histogram construction for reversible data hiding

Reversible data hiding (RDH) algorithms have been extensively employed in the field of copyright protection and information dissemination. Among various RDH algorithms, the multiple histogram modification (MHM) algorithm has attracted significant attention because of its capability to generate high-quality marked images. In previous MHM methods, the prediction error histograms were mostly generated in a fixed way. Recently, clustering algorithms automatically classify prediction errors into multiple classes, which enhances the similarity among prediction errors within the same class. However, the design of features and the determination of clustering numbers are crucial in clustering algorithms. Traditional algorithms utilize the same features and fix the number of clusters (e.g., empirically generate 16 classes), which may limit the performance due to the lack of adaptivity. To address these limitations, an adaptive initial feature selection scheme and a clustering number optimization scheme based on the Fuzzy C-Means (FCM) clustering algorithm are proposed in this paper. The superiority of the proposed scheme over other state-of-the-art schemes is verified by experimental results.

A Survey on Reversible Data Hiding for Uncompressed Images

Reversible data hiding (RDH) has developed various theories and algorithms since the early 1990s. The existing works involve a large amount of specialized knowledge, making it difficult for researchers, especially primary learners, to have a good grounding in the basic ideas. In this survey, we will review the mainstream RDH algorithms in uncompressed images and analyze their unique features to provide readers with an introduction to basic topics in RDH. We analyze the most effective RDH frameworks and their common extensions. The classic techniques, including lossless compression-based RDH, difference expansion, integer transform, histogram shifting, prediction-error expansion (PEE), and their extensions, will be reviewed first. Then, three currently popular investigated schemes, i.e., multiple histograms modification, pairwise PEE, and pixel-value-ordering, are presented in detail. Four aspects of these mainstream techniques are reviewed and analyzed, including the evolution of embedding frameworks, detailed technological features, extensions, and the current state of the art. Furthermore, we look forward the possible future research based on early-age motivations.

Reversible Data Hiding for Color Images Using Channel Reference Mapping and Adaptive Pixel Prediction

Reversible data hiding (RDH) is a technique that embeds secret data into digital media while preserving the integrity of the original media and the secret data. RDH has a wide range of application scenarios in industrial image processing, such as intellectual property protection and data integrity verification. However, with the increasing prevalence of color images in industrial applications, traditional RDH methods for grayscale images are inadequate to meet the requirements of image fidelity. This paper proposes an RDH method for color images based on channel reference mapping (CRM) and adaptive pixel prediction. Initially, the CRM mode for a color image is established based on the pixel variation correlation between the RGB channels. Then, the pixel local complexity context is adaptively selected using the CRM mode. Next, each pixel value is adaptively predicted based on the features and characteristics of adjacent pixels and reference channels, and then data is embedded by expanding the prediction error. Finally, we compare seven existing RDH algorithms on the standard image dataset and the Kodak dataset to validate the advantages of our method. The experimental results demonstrate that our approach achieves average peak signal-to-noise ratio (PSNR) values of 63.61 and 60.53 dB when embedding 20,000 and 40,000 bits of data, respectively. These PSNR values surpass those of other RDH methods. These findings indicate that our method can effectively preserve the visual quality of images even under high embedding capacities.

A Novel 3D Reversible Data Hiding Scheme Based on Integer-Reversible Krawtchouk Transform for IoMT.

To avoid rounding errors associated with the limited representation of significant digits when applying the floating-point Krawtchouk transform in image processing, we present an integer and reversible version of the Krawtchouk transform (IRKT). This proposed IRKT generates integer-valued coefficients within the Krawtchouk domain, seamlessly aligning with the integer representation commonly utilized in lossless image applications. Building upon the IRKT, we introduce a novel 3D reversible data hiding (RDH) algorithm designed for the secure storage and transmission of extensive medical data within the IoMT (Internet of Medical Things) sector. Through the utilization of the IRKT-based 3D RDH method, a substantial amount of additional data can be embedded into 3D carrier medical images without augmenting their original size or compromising information integrity upon data extraction. Extensive experimental evaluations substantiate the effectiveness of the proposed algorithm, particularly regarding its high embedding capacity, imperceptibility, and resilience against statistical attacks. The integration of this proposed algorithm into the IoMT sector furnishes enhanced security measures for the safeguarded storage and transmission of massive medical data, thereby addressing the limitations of conventional 2D RDH algorithms for medical images.

Multi-Hider Reversible Data Hiding Using a Weighted Color Transfer and Modulus Operation

This paper proposes a multi-hider dual-image reversible data-hiding algorithm. Generating dual stego images, the proposed algorithm provides a high embedding capacity, exhibits high image quality, offers reversibility to restore the stego images, and suggests seven levels of secret message extraction. The message embedding in our algorithm contains two phases. In Phase-1, we embed an n-ary secret message vector followed by an m-ary secret message in each pixel using two distinct secret keys when rendering a new image from the source and target images using an optimal weighted color-transfer process. This phase produces a tentative stego image which exhibits a new color appearance, different from the input source image. In Phase-2, we introduce a weighted modulus operation to embed a k-ary secret message vector into a dual-pixel constructed from the tentative stego image. The message concealment is elaborately designed so that the hidden secret messages are intact. This phase produces dual stego images which carry three distinct secret messages. Using legitimate secret keys, an authorized receiver can extract one or parts of secret messages and recover the tentative stego color-transferred image without arousing any suspicion. The experimental results and analysis confirm that our scheme can resist RS and PVD steganalytic attacks. In addition, our algorithm provides both high embedding capacity and better image quality, outperforming its counterparts. To the best of our knowledge, our scheme is the first in the relevant literature that provides multi-hider reversible data hiding, thereby offering various levels of message extraction for secure data communication.

A Comprehensive Analysis Method for Reversible Data Hiding in Stream-Cipher-Encrypted Images

Reversible data hiding in encrypted images (RDHEI), an essential branch of reversible data hiding (RDH), has been in development for more than a decade. For most existing stream-cipher-based RDHEI algorithms, encryption schemes are often not the same; thus, these schemes have different effects on the encrypted images. As a result, it is not reasonable to compare the embedding rates (ERs) of RDHEI algorithms directly as we do for plaintext RDH algorithms. However, to our knowledge, many studies focus on the performance of embedding but neglect the influence of the encryption. To compare the performance of stream-cipher-based RDHEI algorithms more reasonably, this paper proposes a novel comprehensive measure to evaluate state-of-the-art RDHEI algorithms. First, the characteristics of the stream-cipher-based RDHEI algorithms are divided into two categories according to the encryption and embedding processes. Next, we use correlation and redundancy to evaluate the influence of encryption schemes and also use ER, computation complexity, visual quality, and algorithm stability to evaluate the embedding schemes. In the end, in order to combine all six indexes into the final evaluation measure, we standardize each index before applying the radar chart. The experimental results show the evaluation results of the recent RDHEI algorithms via a comprehensive analysis and demonstrate the guiding significance of the proposed method for the current RDHEI algorithm selection.

Efficient Reversible Data Hiding Based on Connected Component Construction and Prediction Error Adjustment

To achieve a good trade-off between the data-embedding payload and the data-embedding distortion, mainstream reversible data hiding (RDH) algorithms perform data embedding on a well-built prediction error histogram. This requires us to design a good predictor to determine the prediction errors of cover elements and find a good strategy to construct an ordered prediction error sequence to be embedded. However, many existing RDH algorithms use a fixed predictor throughout the prediction process, which does not take into account the statistical characteristics of local context. Moreover, during the construction of the prediction error sequence, these algorithms ignore the fact that adjacent cover elements may have the identical priority of data embedding. As a result, there is still room for improving the payload-distortion performance. Motivated by this insight, in this article, we propose a new content prediction and selection strategy for efficient RDH in digital images to provide better payload-distortion performance. The core idea is to construct multiple connected components for a given cover image so that the prediction errors of the cover pixels within a connected component are close to each other. Accordingly, the most suitable connected components can be preferentially used for data embedding. Moreover, the prediction errors of the cover pixels are adaptively adjusted according to their local context, allowing a relatively sharp prediction error histogram to be constructed. Experimental results validate that the proposed method is significantly superior to some advanced works regarding payload-distortion performance, demonstrating the practicality of our method.

Reversible data hiding in JPEG images based on coefficient-first selection

Joint photographic experts group (JPEG) is the most commonly used image format on the Internet, and using reversible data hiding (RDH) technology to verify its authenticity and integrity is of great significance. So far, many RDH algorithms for JPEG image have been proposed, which improve the performance of the algorithm via block sorting, frequency selection and other adaptive strategies. In this paper, a new JPEG RDH method based on coefficient-first selection strategy is proposed. In our new method, the distribution models of alternating current (AC) coefficients belonging to different discrete cosine transform (DCT) coefficient blocks are constructed first, and then the distortion value of each AC coefficient can be calculated through the estimated probability density function of the constructed coefficient model. In the embedding process, the coefficients with low distortion values will be preferentially selected for data hiding. Various experimental results demonstrate that compared with the state-of-the-art JPEG RDH methods, our proposed may have better visual quality and less increase of file storage size.

Adaptive reversible data hiding algorithm for interpolated images using sorting and coding

Existing reversible data hiding (RDH) algorithms based on the interpolation technology (IT) have high embedding capacities and visual qualities. However, they usually embed secret data directly into interpolated pixels sequentially, resulting in that the visual qualities may not be optimal. To address this problem, an adaptive IT-based RDH algorithm is proposed. First, for a given interpolated image, the amount of data that can be embedded into each interpolated pixel is sorted in the ascending order. Next, the secret data to be embedded are recoded and sequentially embedded into interpolated pixels after overflow/underflow preprocessing based on the obtained index. The experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art RDH algorithms based on IT. On average, the average peak signal-to-noise ratio (PSNR) of our algorithm is improved by approximately 25%. In comparison with traditional RDH algorithms using histogram shifting and pixel value ordering, our algorithm also achieves a higher PSNR. In addition, our algorithm can resist histogram and regular singular steganalysis attacks. These results clearly demonstrate that the proposed algorithm is effective.

Improving the Security of Reversible Data Hiding Using Multiple Histogram Modification

Reversible data hiding (RDH) allows carrying secret information in cover media without introducing permanent distortion. For a RDH method, the important performance measurements are embedding capacity and image quality. Since embedding capacity is an important requirement in the field of data hiding, it is necessary to consider the security of data embedding in RDH applications. In general, RDH algorithms usually prefer data embedding in simple image regions with low local complexity. As a result, image degradation is alleviated at the cost of poor embedding security. In this study, a novel RDH method is proposed to embed data into complex image regions, wherein the data hiding becomes more secure in defending against modern steganalysis. To measure regional local complexity, the harmonic mean of directional local variances is employed to combine directional pixel differences. To embed data into complex regions instead of smooth regions, multiple histogram modification is adopted and updated for optimized data embedding with higher complexity. Experiment results show that embedding security is significantly improved with a considerable amount of payload and well-preserved image quality.

An adaptive high capacity reversible data hiding algorithm in interpolation domain

Reversible data hiding (RDH) algorithms using interpolation technology have the advantage of high capacity for single-layer embedding. However, existing algorithms usually embed secret data in the non-reference pixels based on simple additions without considering the properties of context-pixels, which results in that the visual quality of watermarked image is not optimal. To deal with this issue, a novel adaptive high capacity RDH algorithm is proposed. Firstly, a location-dependent weighted image interpolation method is proposed to improve the visual quality of the interpolated image. Then, the interpolated image is divided into overlapping blocks, which are sorted in an ascending order according to their standard deviations; secret data is preferentially embedded in the blocks with small deviation so that image quality is not distorted too much. Finally, to enhance embedding capacity as much as possible, the amount of secret data bits that can be embedded in non-reference pixels is calculated adaptively. We theoretically prove that the embedded secret data can be extracted correctly and there is no pixel overflow or underflow problem. Extensive experimental results showed that the proposed algorithm outperforms several state-of-the-art algorithms. In addition, our algorithm can resist steganalysis attacks, and demonstrated the effectiveness of the proposed algorithm.

New CNN-Based Predictor for Reversible Data Hiding

In this letter, we propose a convolutional neural network (CNN) based predictor for reversible data hiding (RDH). Firstly, a new image division strategy is presented, which can divide the cover image into four independent parts. Via using it, any pixel in each part can be predicted by all its 8-neighbor pixels to generate the preprocessed images. Then, the preprocessed image is fed into a carefully designed CNN-based prediction model to output the predicted image, which is used to build the prediction-error histogram for RDH. Experimental results demonstrate that a sharply distributed prediction-error histogram (i.e., small prediction errors) can be easily obtained by our proposed CNN-based predictor. Furthermore, combining with the classical prediction-error expansion (PEE) embedding strategy, a series of new RDH algorithms with higher visual quality can be formed in contrast to the state-of-the-art RDH schemes.

Reversible data hiding based on high fidelity prediction scheme for reducing the number of invalid modifications

Prediction-error expansion (PEE) has been extensively investigated among different approaches of reversible data hiding (RDH). Most PEE-based RDH approaches, e.g., various extensions of adaptive embedding and optimal expansion-bins selection, utilize local complexity of pixels to manage the expansion manner of prediction errors aiming to decrease the number of shifted pixels. However, this fact is not taken into account that prediction errors are not necessarily proportionate to the local complexity of the pixels. In this paper, we focus on forming a prediction-error histogram in the prediction step that causes fewer invalid modifications and results in the reduction of embedding distortion. Hence, an RDH algorithm based on the PEE is presented that provides a general framework for reducing the number of invalid modifications (NIM). To this mean, a high fidelity prediction scheme is proposed using weibull generalized exponential distribution (WGED) to generate two asymmetric prediction-error histograms (APEH). Then, a two-layer embedding procedure is performed through PEE using each APEH. By utilizing WGED, the proposed predictor changes adaptively based on the desired payload and the given cover image to reduce NIM and embedding distortion. The results of comprehensive experiments using three databases confirm the superiority of the presented approach over some existing methods.

Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area

During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.

A comparative study between PVO-based framework and multi-predictor mechanism in reversible data hiding

Sorting-based reversible data hiding (RDH) methods like pixel-value-ordering (PVO) can predict pixel values accurately and achieve an extremely low distortion on the embedded image. However, the excellent performance of these methods was not well explained in previous works, and there are unexploited common points among them. In this paper, we propose a general multi-predictor (GMP) framework to summarize PVO-based RDH methods and explain their high prediction accuracy. Moreover, by utilizing the proposed GMP framework, a more efficient sorting-based RDH method is given as an example to show the generality and applicability of our framework. Comparing with other PVO-based methods, the proposed example method can achieve significant improvement in embedding performance. It is hopeful that more efficient sorting-based RDH algorithms can be designed according to our proposed framework by designing better predictors and their combination methods.

Generalized Reversible Data Hiding with Content-Adaptive Operation and Fast Histogram Shifting Optimization.

Reversible data hiding (RDH) has become a hot spot in recent years as it allows both the secret data and the raw host to be perfectly reconstructed, which is quite desirable in sensitive applications requiring no degradation of the host. A lot of RDH algorithms have been designed by a sophisticated empirical way. It is not easy to extend them to a general case, which, to a certain extent, may have limited their wide-range applicability. Therefore, it motivates us to revisit the conventional RDH algorithms and present a general framework of RDH in this paper. The proposed framework divides the system design of RDH at the data hider side into four important parts, i.e., binary-map generation, content prediction, content selection, and data embedding, so that the data hider can easily design and implement, as well as improve, an RDH system. For each part, we introduce content-adaptive techniques that can benefit the subsequent data-embedding procedure. We also analyze the relationships between these four parts and present different perspectives. In addition, we introduce a fast histogram shifting optimization (FastHiSO) algorithm for data embedding to keep the payload-distortion performance sufficient while reducing the computational complexity. Two RDH algorithms are presented to show the efficiency and applicability of the proposed framework. It is expected that the proposed framework can benefit the design of an RDH system, and the introduced techniques can be incorporated into the design of advanced RDH algorithms.

Prediction error distribution with dynamic asymmetry for reversible data hiding

Prediction-error expansion (PEE) by employing asymmetric prediction-error distribution (PED) provides an opportunity to decrease the embedding distortion in reversible data hiding (RDH). Nevertheless, its performance is completely dependent on the asymmetry rate of utilized PED which has never been studied before despite its crucial role in the performance of RDH algorithms. In this paper, a new prediction scheme called dynamic asymmetric distribution of error (DADE) is proposed for PEE. DADE predictor produces error distribution with dynamic asymmetry rate proportionate to embedding capacity (EC). In the proposed predictor several PEDs with a variety of asymmetry rates are composed and for a given EC, the PED with the highest asymmetry rate is selected, provided that it fulfills the EC. By increasing the asymmetry rate of the PED, the length of its short tail and the number of prediction errors in that part are reduced. Consequently, data embedding through single side expansion of prediction-error in the shorter side significantly decreases the number of shifted pixels which leads to the reduction of embedding distortion. Experimental results prove that the proposed method reduces the embedding distortion while slightly increases the EC of PEE based methods with asymmetric PED, and outperforms some state-of-the-art methods.

Reversible data hiding for encrypted image based on adaptive prediction error coding

Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block-based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efficiently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS-2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state-of-the-art RDH algorithms in embedding capacity.

A reversible data hiding algorithm for audio files based on code division multiplexing

In this paper, a reversible data hiding algorithm for audio files based on code division multiplexing (CDM) is proposed. In the scheme, the orthogonal spreading sequences are employed to carry the embedding data, and the host audio file can also be recovered completely after the secret message has been extracted accurately. At the same time, according the orthogonal character of the spreading sequences, the secret message can be embedded into the audio files repeatedly, and most elements of the spreading sequences are mutually canceled in the process of multi-level data embedding, which maintains the audio file in good auditory even at high data embedding capacity. Moreover, only the receiver who holds the same spreading sequences, as the sender can restore the embedded data, which enhance the security of the proposed scheme. As shown in the experimental results, the CDM based reversible data hiding (RDH) algorithm for audio files can achieve higher data embedding capacity at the same audio distortion compared with those state-of-the-art audio RDH algorithms.

Efficient Reversible Data Hiding Using Dynamic Variance Mean Sorting and Fitting Weight Rhombus Predictor

Reversible data hiding (RDH) together with sorting is one of the key goals for reducing image distortion. This paper presents an improvement in sorting based on the double-layered RDH algorithm by modifying the dynamic variance mean (DVM) sorting models and applying an effective predictor. Seven models are investigated to achieve suitable sorting with the best prediction error (PE) for each capacity and image. The fitting weight rhombus (FWR) predictor and two-pass testing (TPT) strategy are significantly utilized to achieve small PEs and reduce distortion. The performance of the proposed RDH algorithm is compared with other similar methods using eight different images. Experiment results demonstrate that the sorting performance of the proposed method is superior to those of the previous methods.