Reversible Data Hiding Methods Research Articles

Reversible data hiding based on global and local automatic contrast enhancement of low-light color images

Currently, many scholars have investigated reversible data hiding (RDH) based on automatic contrast enhancement (ACE). For RDH based on ACE (ACERDH), various methods have been proposed on how to better improve image quality. Preserving brightness can prevent the image from being over-enhanced. However, some ACERDH methods that can preserve brightness well cannot sufficiently enhance low-light images. Although some ACERDH methods that cannot preserve brightness well can more sufficiently enhance low-light color images, they lack the consideration of the local region, which is not conducive to enhancing the contrast of the local region. Therefore, a novel RDH method based on global ACE and local ACE is proposed. The global contrast is first improved by equalizing the global histogram. Then the high complexity region is optimized by the proposed RDH method based on double-layer ACE to further enhance the local contrast. The low-light color image can be well enhanced by applying the proposed method to enhance the R, G, and B channels sequentially. Experimental results demonstrated that the proposed method is superior to some existing advanced methods in obtaining better image quality and hiding more secret data.

Global pixel-value-ordering framework with dynamic sequence partition for reversible data hiding

Recently, pixel-value-ordering (PVO) based reversible data hiding (RDH) methods have become hotspots in spatial domain RDH research. In PVO-based methods, the pixel value correlation within a local region is exploited for data embedding. To embed secret data, the cover images are partitioned into non-overlapped rectangle blocks to be sorted locally, and a lower embedding distortion is thus obtained. However, their embedding capacities are often limited by the fixed local blocks from which pixels are sorted. To maintain the advantages and overcome disadvantages of the PVO-based methods, a new global pixel-value-ordering (GPVO) framework is proposed in this paper. By applying our GPVO framework, a PVO-based method can utilize pixels from any position in the cover image rather than from local pixel blocks. Then, through the dynamic sequence partition realized by the GPVO framework for the first time, the embedding capacity of PVO-based methods can be greatly improved while reducing embedding distortion further reduced. Finally, we propose a two-stage pairwise embedding scheme to be applied to the sequences, aiming to achieve an advanced embedding performance. Experimental results illustrate that the proposed GPVO works better than other related state-of-the-art RDH methods.

Reversible data hiding with automatic contrast enhancement for color images

Automatic contrast enhancement (ACE) is a technique that can automatically enhance the image contrast. Reversible data hiding (RDH) with ACE (ACERDH) can achieve ACE while hiding data. However, some methods with good performance for color images suffer from insufficient enhancement. Therefore, an ACERDH method based on the R, G, B, and V channels enhancement is proposed. First, histogram shifting with contrast control is proposed to enhance the R, G, and B channels. It can prevent contrast degradation and histogram shifting from stopping prematurely. Then, the V channel is enhanced. Since some RDH methods with non-ACE that can well enhance the V channel have a low automation level, histogram shifting with brightness control that can realize ACE very well is proposed. It can effectively avoid over-enhancement by controlling the brightness. Experimental results verify that the proposed method improves the image quality and embedding capability better than some state-of-the-art methods.

Inter-Channel Correlation Modeling and Improved Skewed Histogram Shifting for Reversible Data Hiding in Color Images

Reversible data hiding (RDH) is an advanced data protection technology that allows the embedding of additional information into an original digital medium while maintaining its integrity. Color images are typical carriers for information because of their rich data content, making them suitable for data embedding. Compared to grayscale images, color images with their three color channels (RGB) enhance data embedding capabilities while increasing algorithmic complexity. When implementing RDH in color images, researchers often exploit the inter-channel correlation to enhance embedding efficiency and minimize the impact on image visual quality. This paper proposes a novel RDH method for color images based on inter-channel correlation modeling and improved skewed histogram shifting. Initially, we construct an inter-channel correlation model based on the relationship among the RGB channels. Subsequently, an extended method for calculating the local complexity of pixels is proposed. Then, we adaptively select the pixel prediction context and design three types of extreme predictors. The improved skewed histogram shifting method is utilized for data embedding and extraction. Finally, experiments conducted on the USC-SIPI and Kodak datasets validate the superiority of our proposed method in terms of image fidelity.

Influential Metrics Estimation and Dynamic Frequency Selection Based on Two-Dimensional Mapping for JPEG-Reversible Data Hiding.

JPEG Reversible Data Hiding (RDH) is a method designed to extract hidden data from a marked image and perfectly restore the image to its original JPEG form. However, while existing RDH methods adaptively manage the visual distortion caused by embedded data, they often neglect the concurrent increase in file size. In rectifying this oversight, we have designed a new JPEG RDH scheme that addresses all influential metrics during the embedding phase and a dynamic frequency selection strategy with recoverable frequency order after data embedding. The process initiates with a pre-processing phase of blocks and the subsequent selection of frequencies. Utilizing a two-dimensional (2D) mapping strategy, we then compute the visual distortion and file size increment (FSI) for each image block by examining non-zero alternating current (AC) coefficient pairs (NZACPs) and their corresponding run lengths. Finally, we select appropriate block groups based on the influential metrics of each block group and proceed with data embedding by 2D histogram shifting (HS). Extensive experimentation demonstrates how our method's efficiently and consistently outperformed existing techniques with a superior peak signal-to-noise Ratio (PSNR) and optimized FSI.

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.

PVO-Based Reversible Data Hiding Using Global Sorting and Fixed 2D Mapping Modification

Pixel-value-ordering (PVO) is one of the most popular methods in reversible data hiding (RDH). In PVO based methods, pixels are processed in a block-wise way, so that the local similarities of the images are considered but the global statistical characteristics are ignored. To better utilize the correlations of pixels, this paper proposes a global sorting strategy to combine utilizations of local and global characteristics of the images. For each pixel, its prediction value and local complexity are first calculated based on its local characteristics. Then the image pixels are sorted globally according to their prediction values to generate a single-sorted pixel sequence, in which the pixels with the equal prediction values are sorted again by referring to their local complexities. In such a way, the spatial distances of image pixels are broken so that the global statistical characteristics can be well exploited. With the proposed sorting strategy, we can obtain a more regular 2D histogram by segmenting the sorted sequence for the location-based PVO (LPVO) predictor. Owe to the regular 2D histogram, we have designed an efficient 2D mapping to achieve perfect performance for all the tested images. With the proposed RDH scheme, the PSNR of the image Lena is as high as 61.86 dB and the average PSNR of the Kodak dataset reaches 63.55 dB after embedding 10,000 bits. The superiority of the proposed method has been verified by comparing with recent state-of-the-art RDH methods.

HSB based reversible data hiding using sorting and pairwise expansion

Reversible data hiding (RDH) continues to advance in its applicability across diverse fields, including medical imaging, military applications, and cloud computing. In recent times, there has been a proliferation of RDH methods that focus on embedding in the higher significant bit (HSB) plane, as opposed to the least significant bit (LSB), in order to leverage the greater correlation, thereby achieving a larger embedding capacity (EC). This paper introduces a novel and efficient RDH approach that utilizes sorting and pairwise expansion. The proposed method initiates by partitioning the original image into HSB and LSB planes and then converting the HSB plane into a chessboard pattern. Subsequently, pixels are organized in ascending order based on their predicted values, forming groups of 1 × 3 pixels. The embedding process occurs in two passes: the first employs a difference expansion-based RDH, while the second pass employs pairwise expansion to minimize distortion. This method accomplishes an expanded EC while preserving the quality of the resulting image. Experimental results also demonstrate that the proposed approach maintains a significant lead of approximately 0.5 dB over the best-performing existing methods, underscoring its superior embedding efficiency.

Reversible Data Hiding of JPEG Image Based on Adaptive Frequency Band Length

JPEG images are widely used on the Internet. Histogram shifting reversible data hiding (RDH) methods based on quantized DCT (discrete cosine transform) coefficients are a research focus for JPEG images. Among them, frequency band selection is a key step that affects the performance of JPEG image RDH. In the existing algorithm, frequency band selection is to evaluate the embedding performance of the whole frequency band. But after DCT block sorting, the distribution of expand AC (alternating current) coefficients (Coefficients that can carry data) is in the front of the frequency band, so the performance evaluation of the whole frequency band will produce errors. This paper proposed an adaptive frequency band length JPEG image RDH method, which determines the used length of the frequency band while selecting the frequency band, so that can effectively reduce the invalid shift. Instead of selecting the whole frequency band length in a fixed mode for performance evaluation, the optimal combination of frequency band lengths will be selected according to the embedding performance of different lengths of each frequency band. Then, a united solution mechanism is used to solve the optimal frequency band length, and the frequency band selection is also completed while solving the frequency band length. Experimental results show that our algorithm outperforms existing state-of-the-art methods in terms of marked image visual quality and file size increment.

Dynamic Hybrid Reversible Data Hiding Based on Pixel-value-ordering

<p>Reversible data hiding (RDH) using pixel-value-ordering (PVO) is a well-established technique for embedding data in a cover image by modifying the maximum and minimum in each block. This paper proposes a dynamic hybrid RDH method based on PVO. Specifically, a 3×3 block according to its complexity and two thresholds T1 and T2 is classified as three levels: extremely smooth, smooth, and rough. Different processing algorithms are used for different levels. For rough blocks, they are ignored to avoid reducing the peak signal-to-noise ratio. For smooth blocks, the proposed method employs a block subdivision algorithm that can embed up to 6 bits of data. For extremely smooth blocks, no subdivision is done and a median pixel prediction algorithm is used to predict the remaining eight pixels, which can embed up to 8 bits of data. Moreover, this paper presents a new method that computes complexity by dynamically selecting relevant pixels to enhance block classification accuracy. Extensive experiments demonstrate that the proposed method outperforms existing PVO-based methods, offering larger embedding capacity while maintaining low distortion.</p> <p> </p>

Secure medical image watermarking based on reversible data hiding with Arnold's cat map

The process of restoring medical images to their original form after the extraction process in application watermarking is crucial for ensuring their authenticity. Inaccurate diagnoses can occur due to distortions in medical images from conventional data embedding applications. To address this issue, reversible data hiding (RDH) method has been proposed by several researchers in recent years to embed data in medical images. After the extraction process, images can be restored to their original form with a reversible data-hiding method. In the past few years, several RDH methods have been rapidly developed, which are based on the concept of difference expansion (DE). However, it is crucial to pay attention to the security of the medical image watermarking method, the embedded data with RDH method can be easily modified, accessed, and altered by unauthorized individuals if they know the employed method. This research suggests a new approach to secure the RDH method through the use of Chaotic Map-based Arnold's Cat Map algorithms on the medical images. Data embedding was performed on random medical images using a DE method. Four gray-scale medical image modalities were used to assess the proposed method's efficacy. In our approach, we can incorporate capacity up to 0.62 bpp while maintaining a visual quality up to 41.02 dB according to PSNR and 0.9900 according to SSIM. The results indicated that it can enhance the security of the RDH method while retaining the ability to embed data and preserving the visual appearance of the medical images.

High-Quality Reversible Data Hiding Based on Multi-Embedding for Binary Images

Unlike histogram-based reversible data hiding (RDH), the general distortion-based framework considers pixel-by-pixel distortions, which is a new research direction in RDH. The advantage of the general distortion-based RDH method is that it can enhance the visual quality of the marked image by embedding data into visually insensitive regions (e.g., edges and textures). In this paper, following this direction, a high-capacity RDH approach based on multi-embedding is proposed. The cover image is decoupled to select the embedding sequence that can better utilize texture pixels and reduce the size of the reconstruction information, and a multi-embedding strategy is proposed to embed the secret data along with the reconstruction information by matrix embedding. The experimental results demonstrate that the proposed method provides a superior visual quality and higher embedding capacity than some state-of-the-art RDH works for binary images. With an embedding capacity of 1000 bits, the proposed method achieves an average PSNR of 49.45 dB and an average SSIM of 0.9705 on the test images. This marks an improvement of 1.1 dB in PSNR and 0.0242 in SSIM compared to the latest state-of-the-art RDH method.

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.

High-Capacity Reversible Data Hiding Based on Two-Layer Embedding Scheme for Encrypted Image Using Blockchain

In today’s digital age, ensuring the secure transmission of confidential data through various means of communication is crucial. Protecting the data from malicious attacks during transmission poses a significant challenge. To achieve this, reversible data hiding (RDH) and encryption methods are often used in combination to safeguard confidential data from intruders. However, existing secure reversible hybrid hiding techniques are facing challenges related to low data embedding capacity. To address these challenges, the proposed research presents a solution that utilizes block-wise encryption and a two-layer embedding scheme to enhance the embedding capacity of the cover image. Additionally, this technique incorporates a blockchain-enabled RDH method to ensure traceability and integrity by storing confidential data alongside the hash value of the stego image. The proposed work is divided into three phases. First, the cover image is encrypted. Second, the data are embedded in the encrypted cover image using a two-layer embedding scheme. Finally, the stego image along with the hash value are deployed through blockchain technology. The proposed method reduces challenges associated with traceability and integrity while increasing the embedding capacity of images compared to traditional methods.

Reversible data hiding for JPEG images based on block difference model and Laplacian distribution estimation

Reversible data hiding (RDH) for JPEG images has attracted extensive attentions in recent years. As far, a number of related schemes have been proposed, which enhance the embedding performance by improving the strategies such as frequency selection and block sorting to choose appropriate embedding coefficients with the histogram shifting mechanism. Nevertheless, the existing methods do not fully consider the characteristics of DCT coefficients and lack accurate measurement on the embedding distortion. To address the above problems, we propose an improved general framework of RDH for JPEG images based on the Laplacian distribution model. The block difference model is first established for the alternating current (AC) coefficients fitting. Subsequently, the scale parameter is calculated to estimate the embedding efficiency of each AC coefficient, and the coefficients with high embedding efficiency are preferentially selected for embedding. Furthermore, the proposed framework can be combined with other JPEG RDH methods based on quantized DCT coefficient modification. Experimental results demonstrate that better performance in terms of both visual quality and file size preservation can be achieved with the proposed scheme compared with the state-of-the-art RDH schemes for JPEG images.

A prediction error based reversible data hiding scheme in encrypted image using block marking and cover image pre-processing.

A drastic change in communication is happening with digitization. Technological advancements will escalate its pace further. The human health care systems have improved with technology, remodeling the traditional way of treatments. There has been a peak increase in the rate of telehealth and e-health care services during the coronavirus disease 2019 (COVID-19) pandemic. These implications make reversible data hiding (RDH) a hot topic in research, especially for medical image transmission. Recovering the transmitted medical image (MI) at the receiver side is challenging, as an incorrect MI can lead to the wrong diagnosis. Hence, in this paper, we propose a MSB prediction error-based RDH scheme in an encrypted image with high embedding capacity, which recovers the original image with a peak signal-to-noise ratio (PSNR) of dB and structural similarity index (SSIM) value of 1. We scan the MI from the first pixel on the top left corner using the snake scan approach in dual modes: i) performing a rightward direction scan and ii) performing a downward direction scan to identify the best optimal embedding rate for an image. Banking upon the prediction error strategy, multiple MSBs are utilized for embedding the encrypted PHR data. The experimental studies on test images project a high embedding rate with more than 3 bpp for 16-bit high-quality DICOM images and more than 1 bpp for most natural images. The outcomes are much more promising compared to other similar state-of-the-art RDH methods.

Dual-image reversible data hiding method using maximum embedding ability of each pixel

Compared to conventional reversible data hiding (RDH) methods, dual-image RDH methods have greater embedding capacity, better stego image quality and higher security. Among existing dual-image RDH methods, three methods of Lu et al., Yao et al. and Kim et al. are remarkable. These three methods have much higher embedding capacities than other methods, while still maintaining stego image quality. However, these three methods all have a limitation that in order to avoid underflow/overflow, they have to ignore pixels around two pixels with values 0 and 255. In many cases, the number of such pixels is not small and this limits the embedding capacity of all three methods above. In this paper, the maximum number of bits that can be embedded in each pixel is evaluated correctly. Embedding a number of bits less than or equal to the maximum number at each pixel will not cause the underflow/overflow. Therefore the proposed method can embed data bits in all pixels of an original image. As a result, the proposed method has not only a larger embedding capacity, but also better stego image quality than mentioned above methods. With an embedding ratio of 3.45 bits per a pixel, the proposed method has a greater embedding capacity than existing dual image reversible data hiding methods while maintaining good image quality.

Improved Dual-image Quality with Reversible Data Hiding Using Translocation and Switching Strategy

Dual-image reversible data hiding (RDH) is a technique for hiding important messages. This technology can be used to safely deliver secret messages to the recipient through dual images in an open network without being easily noticed. The recipient of the image must receive the two stego-images before the secret message can be completely retrieved. Imperceptibility is one of the main advantages of data hiding technology; to increase the imperceptibility, the quality requirements of the stego-images are relatively important. A dual steganographic image RDH method, called a DS-CF scheme that can achieve a better steganographic image quality using the center folding (CF) strategy. In this paper, we developed a translocation and switching strategy (TaS) to shorten the distances between the stego-pixel coordinates and the cover pixel coordinates after information being hidden. Compared with the DS-CF scheme, our proposed DS-TaS scheme can effectively improve the quality of the steganographic images at the same level of embedding capability. The experimental results show that the PSNR of our DS-TaS scheme at = 1 was 55.66 dB, which is an increase of 1.5 dB, and is 51.43 dB for = 2, 46.66 dB for = 3, and 40.91 dB for = 4. In addition, the PSNR values of the stego images was increased by 1.5, 0.29, 0.29, and 0.19 dB, respectively. This shows that our proposed dual-image RDH method can optimize the visual quality of the stego-images and is better than many other dual-image RDH techniques.

Reversible Data Hiding Method for Interpolated Images Based on Modulo Operation and Prediction-Error Expansion

Image reversible data hiding (RDH) using interpolation technology (IT) provides high single-layer embedding capacity and has become a research hotspot. To solve serious image distortion in existing methods caused by secondary embedding based on interpolated pixels, we propose an RDH method for interpolated images based on modulo operation and prediction-error expansion (PEE). Our method differs from existing RDH methods that only consider embedding data into interpolated pixels. First, an image interpolation method is used to generate an interpolated image, which is partitioned into overlapping 3 × 3 blocks. For each block, secret data are embedded into interpolated pixels using the modulo operation and into reference pixel using the PEE. Standard grayscale images of size 512 × 512 were selected from the popular BossBase and BOWS-2 image databases as input images for experiments. Extensive experimental results show that the proposed method avoids pixel values underflow/overflow caused by embedding data and reduces image distortion effectively. For each standard test image, the maximum bits per pixel (BPP) is higher than 1.5, and the corresponding peak signal to noise ratio (PSNR) is higher than 45.0 dB. At the given embedding capacity, the proposed method outperforms recent similar methods regarding the watermarked image quality. Moreover, the proposed method can resist histogram analysis, and the subjective evaluation proves that the proposed method has good imperceptibility.

General Framework to Reversible Data Hiding for JPEG Images With Multiple Two-Dimensional Histograms

In this paper, a general reversible data hiding (RDH) framework for joint photographic experts group (JPEG) images with multiple two dimensional histograms (2DHs) is proposed. Regardless of whether zero alternating current (AC) coefficients are included to join data embedding or only non-zero AC coefficients are applied, the performance in terms of visual quality and file size increment is improved by using the proposed framework. This framework is mainly composed of the following three parts: histogram generation, adaptive 2DH mapping selection, and improved discrete particle swarm optimization (IDPSO). Unlike existing 2DH-based JPEG RDH methods, in which a uniform threshold is utilized to construct multiple histograms, in histogram generation, thresholds for different histograms are adaptively assigned according to the local properties of histogram coefficients. As a result, as many coefficients in complex regions as possible are excluded from the construction of each histogram. We subtly design multiple 2DH mappings, and adaptively select 2DH mappings for different 2DHs based on their distribution characteristics. Through slight adjustments, each 2DH mapping can be employed in cases where either zero AC coefficients or only non-zero AC coefficients are used for data embedding. Adaptive threshold and 2DH mapping selection provide a better image quality at a given embedding capacity but inevitably cause considerable complexity cost. To significantly reduce the computational cost, we propose IDPSO by combining differential evolution. IDPSO has the advantages of rapid convergence speed as well as satisfactory qualities of the best solutions. With the help of differential evolution, IDPSO expands the diversity of particles and efficiently avoids local optimal trapping problems. The experimental results also demonstrate the effectiveness of the proposed method in terms of visual quality, file size increment and complexity cost.