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Reversible data hiding (RDH) in encrypted images has emerged as an effective technique for securely storing and managing confidential images in the cloud. However, most RDH methods in shared images (RDHSI) are designed for uncompressed images and cannot be applied for JPEG images. To address this issue, we propose a novel RDH in shared JPEG images. Our method consists of JPEG image sharing and data hiding in JPEG shares, which are both conducted on JPEG bit-stream. Specifically, the DC appended bits (DCA) and AC appended bits (ACA) derived from the original JPEG bit-stream are shared by (\(k\) , \(n\)) threshold Chinese remainder theorem-based secret sharing (CRTSS) with two different constraints, one for DC sharing and another for AC sharing. The constraint of DC sharing ensures that the DC coefficient shares do not overflow. The constraint of AC sharing ensures the sizes of ACA shares are less than the sizes of the original ACA so that the embedding room can be vacated from each shared JPEG bit-stream. Each data-hider can embed the secret data into the personal JPEG share. The original JPEG image can be recovered losslessly from any \(k\) JPEG shares. The proposed sharing and data hiding are both well compatible with the JPEG standard. Experimental results demonstrate that the proposed method not only well preserves the file size whether the JPEG shares or marked JPEG shares but also achieves outstanding security performance and a high embedding capacity. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reversible Data Hiding (RDH) in the encrypted domain has been extensively investigated because of its myriad of applications in different fields and security and privacy concerns of secret messages and covers. However, most of the methods mainly suffer due to low embedding capacity, communication overhead, security of secret messages and overflow/underflow issues. In order to address these issues, an efficient separable RDH method employing bit-pair differences has been proposed. Embedding at bit pairs level not only achieves high secret data embedding capacity but also enhances the security and privacy of the secret message and cover. The proposed method employs the average and floor function to generate the interpolated cover and hence, avoids the overflow/underflow problem. The proposed separable method supports blind data extraction from the marked cover thereby being computationally inexpensive. In this, secret messages and covers are recovered independently on the basis of available keys rather than sequence order. This method is computationally inexpensive as the embedding algorithm only consideres simple rules based data hiding and data retrieval is achieved through simple XOR operation which overcomes the communication overhead. The performance of the proposed method is compared with the state-of- the-art methods and results reveal significant improvements in the embedding capacity. THe maximum embedding capacity of 4 bits per pixel(bpp) with an average PSNR value of 34.99 dB is achieved irrespective of the cover. [ABSTRACT FROM AUTHOR]

The rapid growth of Information and communication technology not only has positive impacts but also unveils opportunities for data and information security threats. In recent years, many researchers have worked on developing methods to enhance data security, particularly through data hiding techniques aimed at safeguarding communications by concealing their existence. With the same objective in mind, this study introduces a novel method for Reversible Data Hiding (RDH) based on a combination of difference expansion (DE) and a modulus function. Our method enables the embedding of 3-bit data into 2-bit Least Significant Bit (LSB) difference values of pixel pairs formed in rectangular blocks. Based on the experimental results, the payload capacity of our method can reach 0.3953 bpp with a PSNR of 53.5900 dB on common images and 0.5764 bpp with a PSNR of 52.9234 dB on medical images. Our method consistently achieves high payload capacity with good visual quality, and our method surpasses previous approaches in terms of performance, payload capacity, and visual quality. [ABSTRACT FROM AUTHOR]

As cloud storage and multimedia communication continue to evolve, the preservation of image privacy is becoming increasingly important. Reversible data hiding in encrypted images (RDHEI) is an effective method for enhancing the protection of personal privacy. To improve the embedding capacity, this paper introduces a novel scheme for RDHEI using block-based bit-plane reallocation. First, an effective optimized prediction method is utilized to predict the value of each pixel, followed by the calculation of prediction errors (PEs). Then, block-based bit-planes of PEs are extracted, and ten different bit-plane types are categorized to facilitate bit-plane reallocation, thereby freeing up redundant space. Finally, an adaptive encryption procedure is employed to encrypt the reallocated bit-plane, excluding the flag-bits. Experimental results demonstrate that the proposed scheme achieves average embedding rates (ER) of 3.584 bpp, 3.501 bpp, and 2.985 bpp on the three experimental datasets, all of which are higher than those of other state-of-the-art RDHEI schemes while ensuring both reversibility and security. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) is an approach that emphasizes the imperceptibility of hidden confidential data and the restoration of the original cover image. To achieve these objectives at the same time, in this paper, we design a matrix-based crossover data hiding strategy and then propose a novel matrix-based RDH scheme with dual meaningful image shadows, called MRA-VSS (matrix-based reversible and authenticable visual secret-sharing). Each pixel in a secret image is divided into two parts, and each part is embedded into a cover pixel pair by referring to the intersection point of four overlapping frames. During the share construction phase, not only partial information of the pixel in a secret image but also authentication codes are embedded into the corresponding cover pixel pair. Finally, two meaningful image shadows are derived. The experimental results confirm that our designed MRA-VSS successfully embeds pixels' partial information and authentication code into cover pixel pairs at the cost of slight distortion during data hiding. Nevertheless, the robustness of our scheme under the steganalysis attack and the authentication capability of our scheme are also proven. [ABSTRACT FROM AUTHOR]

The fields of image processing and computer vision have witnessed significant growth due to the proliferation of digital images across diverse domains. Image Segmentation is the fundamental task in digital image processing, finding applications in pivotal areas such as medical imaging, covert communication, autonomous driving, satellite imaging, among others. One particularly intriguing application of image segmentation lies in Reversible Data Hiding (RDH), where the delineation of the main Region of Interest (ROI) and Non-Region of Interest (NROI) using segmentation plays a crucial role for effective data encryption in the images. Over the last two decades, various studies focussed on developing an efficient data hiding approach, which can embed secret data within ROI and NROI part of image while ensuring its quality. A comprehensive survey has been conducted that meticulously examines different segmentation techniques, along with its usage in reversible data hiding. The main objective of this survey is to compare the performance metrics of reversible data hiding after applying different image segmentation techniques. The image segmentation techniques have been categorized systematically into three main classes: i) Traditional segmentation techniques, encompassing a spectrum of approaches like thresholding, region-based and edge detection based techniques, ii) Machine Learning (ML) based approach consisting of Clustering, Support Vector Machine (SVM) and iii) Deep Learning (DL) based technique, propelled by Convolutional Neural Networks (CNNs) that have emerged as a transformative paradigm, revolutionizing segmentation tasks with their ability to learn complex images. The survey finds out that PSNR value of data embedded images is high after applying deep learning based segmentation technique. [ABSTRACT FROM AUTHOR]

With the rapid advancement of cloud computing technology, reversible data hiding algorithms in encrypted images (RDH-EI) have developed into an important field of study concentrated on safeguarding privacy in distributed cloud environments. However, existing algorithms often suffer from low embedding capacities and are inadequate for complex data access scenarios. To address these challenges, this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection (AMED) and ciphertext-policy attribute-based encryption (CP-ABE). This proposed algorithm enhances the conventional median edge detection (MED) by incorporating dynamic variables to improve pixel prediction accuracy. The carrier image is subsequently reconstructed using the Huffman coding technique. Encrypted image generation is then achieved by encrypting the image based on system user attributes and data access rights, with the hierarchical embedding of the group’s secret data seamlessly integrated during the encryption process using the CP-ABE scheme. Ultimately, the encrypted image is transmitted to the data hider, enabling independent embedding of the secret data and resulting in the creation of the marked encrypted image. This approach allows only the receiver to extract the authorized group’s secret data, thereby enabling fine-grained, controlled access. Test results indicate that, in contrast to current algorithms, the method introduced here considerably improves the embedding rate while preserving lossless image recovery. Specifically, the average maximum embedding rates for the (3, 4)-threshold and (6, 6)-threshold schemes reach 5.7853 bits per pixel (bpp) and 7.7781 bpp, respectively, across the BOSSbase, BOW-2, and USD databases. Furthermore, the algorithm facilitates permission-granting and joint-decryption capabilities. Additionally, this paper conducts a comprehensive examination of the algorithm’s robustness using metrics such as image correlation, information entropy, and number of pixel change rate (NPCR), confirming its high level of security. Overall, the algorithm can be applied in a multi-user and multi-level cloud service environment to realize the secure storage of carrier images and secret data. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The rapid growth of Information and communication technology not only has positive impacts but also unveils opportunities for data and information security threats. In recent years, many researchers have worked on developing methods to enhance data security, particularly through data hiding techniques aimed at safeguarding communications by concealing their existence. With the same objective in mind, this study introduces a novel method for Reversible Data Hiding (RDH) based on a combination of difference expansion (DE) and a modulus function. Our method enables the embedding of 3-bit data into 2-bit Least Significant Bit (LSB) difference values of pixel pairs formed in rectangular blocks. Based on the experimental results, the payload capacity of our method can reach 0.3953 bpp with a PSNR of 53.5900 dB on common images and 0.5764 bpp with a PSNR of 52.9234 dB on medical images. Our method consistently achieves high payload capacity with good visual quality, and our method surpasses previous approaches in terms of performance, payload capacity, and visual quality.

The fields of image processing and computer vision have witnessed significant growth due to the proliferation of digital images across diverse domains. Image Segmentation is the fundamental task in digital image processing, finding applications in pivotal areas such as medical imaging, covert communication, autonomous driving, satellite imaging, among others. One particularly intriguing application of image segmentation lies in Reversible Data Hiding (RDH), where the delineation of the main Region of Interest (ROI) and Non-Region of Interest (NROI) using segmentation plays a crucial role for effective data encryption in the images. Over the last two decades, various studies focussed on developing an efficient data hiding approach, which can embed secret data within ROI and NROI part of image while ensuring its quality. A comprehensive survey has been conducted that meticulously examines different segmentation techniques, along with its usage in reversible data hiding. The main objective of this survey is to compare the performance metrics of reversible data hiding after applying different image segmentation techniques. The image segmentation techniques have been categorized systematically into three main classes: i) Traditional segmentation techniques, encompassing a spectrum of approaches like thresholding, region-based and edge detection based techniques, ii) Machine Learning (ML) based approach consisting of Clustering, Support Vector Machine (SVM) and iii) Deep Learning (DL) based technique, propelled by Convolutional Neural Networks (CNNs) that have emerged as a transformative paradigm, revolutionizing segmentation tasks with their ability to learn complex images. The survey finds out that PSNR value of data embedded images is high after applying deep learning based segmentation technique.

Reversible data hiding in encrypted images (RDH-EI) integrates encryption with information hiding, enabling the embedding of additional data while ensuring full recovery of the original image, widely used in multimedia data protection and forensics. However, with the increasingly serious problem of data islands, the existing RDH-EI schemes for end-to-end communication scenarios can no longer meet the application requirements for multi-party data sharing. For solving this problem, this paper proposes an RDH-EI method based on Lasso regression predictor and dynamic secret sharing. First, Lasso regression predictor is proposed, which avoids the problem of over-fitting by regularizing the loss function. Then, to reserve more embeddable room, we map the prediction error and use arithmetic coding for compression. Next, the original image and auxiliary information are shared through dynamic secret sharing algorithm, and they can be transmitted to the corresponding data hiders respectively. According to the reserved room, each data hider allows for the discrete implantation of the secret bits into the corresponding encrypted image. The receiver can successfully extract the secret bits and recreate the cover image without distortion by gathering a specific percentage of marked images. Experimental results demonstrate that the suggested Lasso regression predictor has a greater prediction accuracy, outperforming contemporary methods for embedding rate. Additionally, it has great security and strong key sensitivity. [ABSTRACT FROM AUTHOR]

The edge area of an image is more resilient to distortion caused by embedding in steganography, driving the advancement of edge detection-based methods. State-of-the-art techniques, including hybrid, dilation, and Fuzzy-based approaches, have been developed to enhance steganography performance. Typically, these methods categorize image pixels into two regions: edge and non-edge areas. This research introduces graded Fuzzy edge detection, classifying pixels into main, supporting, and non-edge categories. Consequently, the embedding priority and message bit determination become more diverse and adaptive. Experimental results demonstrate that graded Fuzzy edge detection optimizes imperceptibility while preserving security and capacity. This technique is combined with various steganographic and reversible data hiding (RDH) methods, exhibiting improvements in stego image quality with over 1dB enhancement based on PSNR, as well as maintaining payload capacity and security aspects according to RS and PDH steganalysis. [ABSTRACT FROM AUTHOR]

In today’s digitally-driven world flooded with digital content and the widespread availability of images online, digital image watermarking is crucial. It ensures copyright protection, maintains content authenticity, and preserves the integrity of visual information. In this paper, a reversible watermarking technique is proposed based on splitting the gray scale image pixel’s intensity value and conventional prediction error expansion. To begin with, the pixel values of an image are split and posted into two distinct groups. The values in the hundred’s and ten’s place are categorized as the higher order pixel group (α ), while the values in the units place are categorized as lower order pixel group (β ). Through this splitting process, it has been noticed that numerous adjacent pixels share identical values. In order to embed data, the conventional Prediction Error Expansion technique is employed specifically for the higher order pixel group. After determining the prediction error, it will be expanded and secret data is embedded into it. This scheme has been experimentally verified that it is superior than few existing state of art schemes. With a high embedding capacity of about 65335 bits in a single 512 × 512 image, the watermarked images exhibited a PSNR of approximately 36.34 dB and an SSIM of around 0.94. Payloads for 10,000 and 20,000 bits are tested and their PSNR values are 44.56 dB and 41.35 dB respectively. [ABSTRACT FROM AUTHOR]

In the last decade, data hiding in encrypted image has got a lot of attention within the security and privacy researcher's communities. This attention has led to the development of various reversible data hiding techniques for encrypted images (RDHEI) which enables separable lossless recovery of cover media and extraction of the secret message, at the receiving end. In this paper, a link chain driven (LCD) RDHEI algorithm for providing high embedding capacity, is proposed. The LCD-RDHEI exploits neighbourhood similarity of image pixels and clubs them in variable size link chains. The pixels in each link chain are estimated either by their average or using two quantization values based on the size of link chains. This helps in reserving a big room inside the image which can be used to fill in the secret information. Additionally, the pixels which are not part of any of the link chains are also utilized for embedding the secret information by adaptive multi MSB prediction and Huffman Coding method. Experimental results show that the LCD-RDHEI provides significantly greater embedding capacity than the existing and related modern data hiding method for encrypted images. [ABSTRACT FROM AUTHOR]

As a booming technique that allows secret data extraction and information carrier recovery without any loss, reversible data hiding in different carriers has attracted more and more concerns in the field of information security. In this paper, a separable reversible data hiding technique for encrypted 3D meshes is proposed based on self-organized blocking and most significant bit (MSB) prediction. The content-owner traverses all faces of the mesh in the ascending index order. Through self-organized blocking, adjacent vertices are concentrated in different small sets. The central vertex is considered as the reference and the others as embedded vertices in each set. Then, multiple most significant bits between the central vertex and others are adaptively predicted and reserved as embeddable bits for secret data embedding. Because vertex coordinates in each set have a high space correlation and most vertices participate in the prediction process, a huge number of most significant bits can be marked as embeddable bits to embed secret data. Experimental results demonstrate that the proposed method can obtain the highest embedding rate compared with representative methods. To our best knowledge, the average embedding rate of the proposed method is about 28 bits per vertex (bpv) higher than the recently proposed method. Most importantly, instead of recovering meshes with higher quality, original meshes with high visual symmetry/quality can be recovered. [ABSTRACT FROM AUTHOR]

In reversible image authentication (RIA) methods, it is common to use a reversible hiding method to embed the authentication code (AC) bits into each block of an original image to form a watermarking image. Since the reversible hiding methods have low embedding capacity, the authentication methods based on them have limited attack detection ability. In this paper, we use a dual-image reversible embedding method based on the central folding strategy with some slight improvements to construct an RIA method. Due to the large embedding capacity, low image distortion, no location map to be used, and AC bits generated by using hash function MD5 on block's features, the proposed image authentication method can detect any type of attack with higher accuracy compared to existing methods. Its attack detection ability reaches 100 % for block sizes of 4 × 4 , 3 × 3 , and 99.91 % for a block size of 2 × 2 while maintaining high image quality. [ABSTRACT FROM AUTHOR]

In the current distributed environment, reversible data hiding in encrypted domain (RDH-ED) cannot grant corresponding privileges according to users' identity classes. To address this issue, this paper proposes a hierarchical authorization structure embedding scheme based on secret image sharing (SIS) and users' hierarchical identities. In the first embedding, the polynomial coefficient redundancy generated in the encryption process of the SIS is utilized by the image owner. For the second, the participants are categorized into two parts. One is core users with adaptive difference reservation embedding, and the other is ordinary users with pixel bit replacement embedding. At the time of reconstruction, more than one core user must provide pixel differences, which grants more privileges to core users. The experimental results demonstrate that the average embedding rate (ER) of the test images is 4.3333 bits per pixel (bpp) in the (3, 4) threshold scheme. Additionally, the reconstructed image achieves a PSNR of +∞ and an SSIM of 1. Compared to existing high-performance RDH-ED schemes based on secret sharing, the proposed scheme with a larger ER maintains strong security and reversibility. Moreover, it is also suitable for multiple embeddings involving multilevel participant identities. In conclusion, the results underscore the efficacy of our technique in achieving both security and performance objectives within a complex distributed setting. [ABSTRACT FROM AUTHOR]

Data hiding in digital images is a potent solution for covert communication, embedding sensitive data into cover images. However, most existing methods are tailored for one-to-one scenarios, which present security risks. To mitigate this vulnerability, we introduce an innovative one-to-two data hiding scheme that employs a two-tier bit-flipping strategy to embed sensitive data in dual images. This process produces two stego images which are then transmitted to two distinct recipients who cannot extract any sensitive data alone. The sensitive data can only be extracted when the two recipients trust each other. Through this method, we can secure the stego images. The experimental results illustrate that our method achieves an excellent data payload while maintaining high visual quality. [ABSTRACT FROM AUTHOR]

In the rapidly evolving fields of artificial intelligence and various industries, the secure processing and management of massive data have become paramount. This paper introduces an innovative reversible data hiding (RDH) method that leverages a Convolutional Neural Network (CNN)-based predictor to generate a predicted image from a given cover image. The secret data are ingeniously embedded within the differences in pixel values between the cover and predicted images. Our experimental analysis reveals a notable reduction in image distortion with increasing secret data size, showcasing the method's potential for diverse applications. The unique aspect of our approach lies in the proportional relation between the Peak Signal-to-Noise Ratio (PSNR) and Embedding Capacity, highlighting its efficacy and efficiency in reversible data hiding. [ABSTRACT FROM AUTHOR]

Secret sharing is a data security technique that divides secret information into multiple parts, embeds these parts into various shares, and distributes these shares to different participants. The original secret information can be retrieved only when the number of shares gathered meets a required threshold. This paper proposes a secret sharing method that can hide data in encrypted images with reversibility and allows content owners to add an additional layer of security before uploading data to the cloud. This method enables the independent extraction of images and data, ensuring that the recovered images and extracted data can serve as validation information for each other. The proposed method not only enhances data security but also guarantees the accuracy of the extracted information. [ABSTRACT FROM AUTHOR]

Multimedia data protection has grown in importance over the past few years. Data concealing methods or encryption can be used to accomplish this. Data compression is required to minimize transmission time; however, combining data compression, encryption, and data concealing in one step presents a new difficulty. Few methods for combining picture compression and encryption have been put forth. Currently, data embedding in encrypted pictures is a hurdle. Standard data hiding strategies are inapplicable for encrypted images as their entropy is maximum since the embedding stage is regarded as noise. This research provides a new approach to reversible data embedding in encrypted photos that preserves picture quality while guaranteeing data security. Three primary stages make up the proposed scheme: data embedding, picture recovery/extraction, and image encryption. The suggested method achieves good picture quality and low computing complexity while retaining strong security characteristics, as shown by the experimental findings. This method works especially well for applications like digital forensics, medical imaging, and secure communications that need to hide data securely and reversibly. [ABSTRACT FROM AUTHOR]

This paper proposes a reversible data embedding algorithm in encrypted images of cloud storage where the embedding was performed by detecting a predictor that provides a maximum embedding rate. Initially, the scheme generates trail data which are embedded using the prediction error expansion in the encrypted training images to obtain the embedding rate of a predictor. The process is repeated for different predictors from which the predictor that offers the maximum embedding rate is estimated. Using the estimated predictor as the label the Convolutional neural network (CNN) model is trained with the encrypted images. The trained CNN model is used to estimate the best predictor that provides the maximum embedding rate. The estimation of the best predictor from the test image does not use the trail data embedding process. The evaluation of proposed reversible data hiding uses the datasets namely BossBase and BOWS-2 with the metrics such as embedding rate, SSIM, and PSNR. The proposed predictor classification was evaluated with the metrics such as classification accuracy, recall, and precision. The predictor classification provides an accuracy, recall, and precision of 92.63%, 91.73%, and 90.13% respectively. The reversible data hiding using the proposed predictor selection approach provides an embedding rate of 1.955 bpp with a PSNR and SSIM of 55.58dB and 0.9913 respectively. [ABSTRACT FROM AUTHOR]

The reversible data hiding scheme in the encrypted domain is a potential solution to the concerns regarding user privacy in cloud applications. The 3D mesh model is an emerging file format and is widely used in engineering modeling, special effects, and video games. However, studies on reversible data hiding in encrypted 3D mesh models are still in the preliminary stage. In this paper, two novel techniques, multi-group partition (MGP) and closest pair prediction (CPP), are proposed to improve performance. The MGP technique adaptively classifies vertices into reference and embeddable vertices, while the CPP technique efficiently predicts embeddable vertices and generates shorter recovery information to vacate more redundancy for additional data embedding. Experimental results indicate that the proposed scheme significantly improves the embedding rate compared to state-of-the-art schemes and can be used in real-time applications. [ABSTRACT FROM AUTHOR]

Reversible data hiding within encrypted images reversible data hiding in encrypted images (RDH-EI) is a highly effective technique for image processing in the field of encryption. This paper, propose a RDH-EI technique, which utilizes bit-plane compression and various image scanning directions to generate vacant space for data embedding, referred to as vacating room. Initially, the prediction error of the pre-processed image is computed. Subsequently, each bit-plane image is converted into a bit-stream by following the pixel scan order employed before compression. The compressed image is then encrypted employing a stream cipher. Through the process of substitution, the secret data and additional information are incorporated into the acquired image without any knowledge of the original content or the encrypted key. Finally, the generated image is transmitted or archived. The experiments provide evidence that the proposed method surpasses the most advanced methods currently available. [ABSTRACT FROM AUTHOR]

Security is vital issues to be communicated for execution of e-healthcare scheme. Till now data hiding has most powerful instrument that can handle previous challenges. Reversible data hiding (RDH) planned to embed secret message in cover image at receiving end, both secret message and original cover image is recovered. Previous RDH methods designed for higher embedding rate. In this work, Deep Neural Rhombus Histogram based Dual Reversible Data Hiding (DNRH-DRDH) is proposed method for addressing both embedding rate as well as visual quality. It utilizes interpolated images reversible data hiding with Block Truncated Rhombus Prediction. Prediction error histogram generated embedded with secret data for obtaining stego images of corresponding interpolated pixels. Lastly, classification output is generated by ReLU activation function in extracted part for secured communication among users. Simulation carried on medical test images. Study reveals of proposed method gives higher embedding rate with superior image quality than existing methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Pixel value ordering (PVO) and prediction error expansion (PEE) techniques are now being combined for improved performance in the domain of reversible data hiding (RDH). Conventional PVO can only embed two data bits in a smooth picture block in a row or column, which is insufficient to meet the contemporary demands of much payload in RDH. There is a lot of room to enhance the bit stuffing by inserting more than two data bits in a smooth block while maintaining acceptable visual image quality and data security. In this study, we suggest a context pixel-based data concealing strategy based on PVO combined with PEE, in which each block can insert two or more data bits. This proposed approach has three distinct features: smooth block selection, minimum and maximum position selection, and pixel difference with median value for data embedding. Furthermore, more than two pixels can be used to embed the secret data bit. The block is recommended by the selection of the neighbouring and present block's pixel. The use of context pixels in context pixel-based PVO (CPPVO) improves both embedding efficiency and picture quality. When the block size is Z = (3 × 3) , the average embedding capacity (EC) of our novel approach is over 70,000 bits, and the average PSNR is over 40 dB. The results of the experiments reveal that our technique is more dependable than other state-of-the-art systems in terms of security, picture quality, and secret volume. [ABSTRACT FROM AUTHOR]

For reversible data hiding in encrypted image (RDHEI), it is an important problem to resolve the contradiction between embedding capacity and image visual quality. However, existing schemes do not maintain a good balance between them. Accordingly, we propose a multi-level block selection embedding mechanism to solve this problem. Our scheme firstly divides original image into muliple non-overlapping blocks with identical size. The most significant bit (MSB) planes of all pixels in the block are predicted from their neighboring pixels to generate a corresponding prediction error map for each block. Subsequently, the pixels are divided into eight bit planes, and the adjacent bit planes are implemented XOR operation to vacate room. Furthermore, by simulating data embedding in each block, PSNR values of each image block are sequentially generated. After encrypting the image using stream ciphers, the optimal blocks with maximum PSNR value can be selected to embed data according to the secret data capacity. Since our proposed scheme usually selects the image block with maximum PSNR value to embed secret data, it can naturally improve the image visual quality. Extensive experiments demonstrate that our scheme is effective and efficient, and outperforms existing state-of-the art methods with better visual quality and higher security performance under the given embedding capacity. [ABSTRACT FROM AUTHOR]

Reversible data hiding over encrypted images (RDH-EI) technology is a viable solution for privacy-preserving cloud storage, as it enables the reversible embedding of additional data into images while maintaining image confidentiality. Since the data hiders, e.g., cloud servers, are willing to embed as much data as possible for storage, management, or other processing purposes, a large embedding capacity is desirable in an RDH-EI scheme. In this article, we introduce a novel bi-directional block encoding (BDBE) method, which, for the first time, encodes the distances of values in a binary sequence from both ends. This approach allows for encoding images with smaller sizes compared to traditional and state-of-the-art encoding methods. Leveraging the BDBE technique, we propose a high-capacity RDH-EI scheme. In this scheme, the content owner initially predicts the image pixels and then employs BDBE to encode the prediction errors, creating space for data embedding. The resulting encoded data are subsequently encrypted using a secure stream cipher, such as the Advanced Encryption Standard, before being transmitted to a data hider. The data hider can embed confidential information within the encrypted image for the purposes of storage, management, or other processing. Upon receiving the data, an authorized receiver can accurately recover the original image and the embedded data without any loss. Experimental results demonstrate that our RDH-EI scheme achieves a significantly larger embedding capacity compared to several state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

With the growing demands of cloud computing and privacy protection, reversible data hiding in encrypted images (RDHEI) has been gradually developed. This paper proposes a RDHEI scheme based on reserving room before encryption (RRBE). The scheme is divided into three types of users: image owner, data hider and receiver. First, the image owner preprocesses the image using a median-prediction-based pixel value ordering algorithm (M-PVO), and then encrypts the image using the Paillier homomorphic cryptosystem, and the data hider performs two stages of data embedding on the encrypted image. According to the key held by the receiver, the data embedded in different stages can be extracted and the original image can be recovered lossless. The experimental results show that the embedding rate of the first stage is about 0.7 bpp, and the embedding rate of the second stage can reach 12 bpp. Compared with other schemes, the proposed scheme has better image quality at the same embedding rate. [ABSTRACT FROM AUTHOR]

As a booming technique in the field of information security, reversible data hiding in encrypted images has attracted great concern because secret data can be recorded in the encryption domain without knowing the related encryption key and the content of original images. In this paper, a fully reversible data hiding method with high embedding capacity is proposed based on optimal block allocation with full consideration of distinct pixel characteristics. The occurrence frequency of most significant bits in smooth blocks is used to obtain embeddable space. For fluctuant blocks, a cascaded two Huffman coding is innovatively designed to compress most significant bits of each pixel to generate more spare rooms. To enhance the security, not only the original image is encrypted based on sequences generated with the high-dimension chaotic system, but also the encrypted image containing secret data is further scrambled. Compared with some state-of-the-art methods, the experimental results and analysis demonstrate that the proposed method has larger embedding capacity using different test images and image databases. [ABSTRACT FROM AUTHOR]

With the development of digital media, its security has also been emphasized, and reversible data hiding (RDH) algorithms has been widely studied. This paper studied RDH algorithms for JPEG images, designed a method to embed secret information by frequency band selection based on discrete cosine transform (DCT) coefficients, called an improved RDH (IRDH) algorithm, and conducted experiments on the USC-SIPI image set. It was found that the designed RDH algorithm had a higher embedding capacity, with an average embedding capacity of 37 031 bits at quantization factor = 50, as well as a higher peak signal-to-noise ratio (PSNR) value and a lower file size increase (FSI) value, compared with the other methods; the dispersion degree of the data obtained by this algorithm was also low. The results demonstrate the reliability of the IRDH algorithm on JPEG images, which can be extended and applied in practice. [ABSTRACT FROM AUTHOR]

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.

The JPEG format is widely used for its high compression rate in image storage.Privacy protection schemes applied to JPEG images not only need to safeguard the content of the privacy region but also maintain the file size.Furthermore, the region of interest (ROI), which contains private information, often occupies only a portion of the entire JPEG image.Protecting only the ROI can help preserve the file size.In light of this, an adaptable privacy protection scheme for JPEG images was proposed.Privacy regions were identified using methods like face detection within the JPEG images.These regions were then modified using the block-based adaptable reversible modification algorithm presented in this study.Reconstruction information was embedded into the image through reversible data hiding.The application of reversible algorithms in the privacy protection process enabled authorized users to fully restore the protected area.By introducing variable parameters, this scheme allowed for the selection of appropriate parameters to meet different practical requirements and achieve optimal encryption results.Additionally, the complex scenario of multiple overlapping ROIs was discussed and resolved.Experimental evaluations were conducted to investigate the influence of parameters on performance, considering both peak signal-to-noise ratio (PSNR) and file size increment.The results demonstrate that the proposed scheme outperforms existing schemes in terms of privacy protection and file size when suitable parameters are employed.

The discrete Charlier moment transform, while extensively utilized in image processing, is inherently lossy and non-integer reversible, making it unsuitable for lossless image applications. To address this, we propose the Integer Reversible Charlier Transform (IRCT), which operates on integer values and produces integer coefficients, enabling perfect and unique recovery of the original input data. The IRCT maintains the orthogonality and invertibility ensuring exact similarity between original and reconstructed images. We leverage the capabilities of the IRCT to develop a novel reversible data hiding (RDH) scheme. This scheme embeds additional data into images by modifying the histogram in the IRCT domain. By capitalizing on the concentrated nature of the IRCT histogram, characterized by high peaks, our method achieves a significantly high embedding capacity while preserving image quality and robustness to statistical attacks. Comparative performance evaluations underscore the effectiveness of the IRCT-based RDH scheme over existing techniques across various domains, positioning it as a promising solution for secure data transmission.

In recent years, with the widespread use of information networks, a significant amount of important information is transmitted through the internet. However, the data transmission process is prone to hacker attacks, leading to data corruption and tampering. Therefore, the secure transmission of important information has become increasingly crucial. Consequently, it is essential to find ways to prevent hackers from detecting the existence of confidential information. The dual-image data hiding technique involves embedding important information into cover images, which are then transmitted to the data receiver. During the transmission process, the hidden information remains undetected by hackers. Therefore, the methods used to embed secret information into the cover images, ensuring both the quality of the embedding and the amount of hidden data, are crucial. This study proposes a novel data hiding method that employs the Vector Coordinate with Triangular Order Coding (TOC) technique to achieve reversible data hiding (RDH) in dual images. Through the TOC method, coordinate vectors can be converted into a decimal value for extracting secret information. Conversely, a decimal value can be transformed into vector coordinates using the TOC method for embedding secret information. Utilizing the TOC method not only achieves excellent stego image quality and a higher data embedding capacity but also eliminates the need to record any additional data, such as codebooks or location maps, during the embedding and extraction processes. Additionally, this method allows setting the k value based on the amount of secret information, where a larger k value enables embedding a greater amount of data, providing increased flexibility for the information hider. Experimental results indicate that when $k=2$ (1 bpp), the average PSNR is 55.66 dB, and when $k=10$ (2.5 bpp), the PSNR is 34.78 dB. This demonstrates that the proposed method can achieve high-quality stego images, and even at $k=10$ (2.5 bpp), the stego image remains imperceptible to the human eye. Our proposed method strikes a balance between quality and capacity, meeting the stringent requirements for secure transmission.

Reversible data hiding (RDH) is an approach that emphasizes the imperceptibility of hidden confidential data and the restoration of the original cover image. To achieve these objectives at the same time, in this paper, we design a matrix-based crossover data hiding strategy and then propose a novel matrix-based RDH scheme with dual meaningful image shadows, called MRA-VSS (matrix-based reversible and authenticable visual secret-sharing). Each pixel in a secret image is divided into two parts, and each part is embedded into a cover pixel pair by referring to the intersection point of four overlapping frames. During the share construction phase, not only partial information of the pixel in a secret image but also authentication codes are embedded into the corresponding cover pixel pair. Finally, two meaningful image shadows are derived. The experimental results confirm that our designed MRA-VSS successfully embeds pixels’ partial information and authentication code into cover pixel pairs at the cost of slight distortion during data hiding. Nevertheless, the robustness of our scheme under the steganalysis attack and the authentication capability of our scheme are also proven.