Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm

A high capacity reversible data hiding scheme based on right-left shift

Recently, Li et al. proposed a data hiding method based on pixel value ordering (PVO) and prediction error expansion (PEE). In their method, maximum and minimum values were predicted and the pixel values were modified to embed secret data. Thereafter, many scholars have proposed improvisations to the original PVO method. In this paper, a Reversible data hiding (RDH) method is proposed where the secret data is dispersed into two layers using different modes of operations. The second layer changes the dividing mode, and the first and the second layers do not take duplicate blocks. Under a fixed embedding capacity, threshold value and block size are controlled, complex blocks are filtered and the secret data is hidden in smooth blocks. This paper also compares the effectiveness of four well-known PVO series methods, the latest PVO methods, difference expansion (DE) method and reduced difference expansion (RDE) method. Experimental results show that the proposed method reduces distortion in the image, thereby enhancing the visual symmetry/quality compared to previous state-of-the-art methods and increasing its high application value.

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 infty 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.

Recently, artificial intelligence (AI) algorithm has been extensively utilized as an optimization tool in digital watermarking. However, existing works seldom consider the reversibility of the embedding framework, thereby neglecting the protection of sensitive carriers. In this paper, we propose an AI-assisted reversible data hiding (RDH) method based on reinforcement learning. In our method, the Q-Learning algorithm is introduced to address the optimization problem of RDH, i.e., adaptive two-dimensional (2D) mapping generation, and it is designed to simulate pixel modifications in 2D space, employing a Markov decision process formulation within the reinforcement learning paradigm. The new reward function is given to evaluate the effectiveness of 2D mapping based on the estimated embedding capacity and the distortion-capacity ratio. To ensure reversibility, a mapping adjustment strategy is implemented to update the environmental states. Experimental results show that the proposed method outperforms conventional 2D RDH and demonstrates competitive performance compared to other state-of-the-art RDH methods.

Opposite to most state-of-the-art reversible data hiding (RDH) methods which usually embed data into smooth area for pursuing high PSNR values of the marked image, we propose a new RDH method for improving visual quality of the details information in medical image instead of pursuing high PSNR. The proposed method embeds data into the texture area preferential by the histogram shifting (HS) method and contrast enhancement method to enhance contrast of texture area and improve the image quality in subjective perception. Our experimental results on medical images show that the proposed method can improve image quality when compared with other HS based RDH method.

Dual-image reversible data hiding (RDH) schemes typically hide secret data in the cover image to generate two marked images that must be received by two trusted receivers over public transmission channels. To measure the performance of an RDH method, the two important indicators are embedding capacity and visual quality. However, existing dual-image RDH studies usually cannot keep a better trade-off between them. To address this issue and improve data security, we propose an efficient dual-image RDH scheme based on Sudoku block mapping in this paper. It is worth noting that our scheme can nicely balance the two indicators, presenting a high embedding ratio at 1.5 bits per pixel at most and better visual quality in lower payload circumstances compared with state-of-the-art dual-image RDH schemes. The average peak signal-to-noise ratio of the proposed scheme on tested images is as high as 68.70 dB for the given payload of 20,000 bits.

Protection of medical data has become a prerequisite in medical imaging clouds due to the semi-trusted cloud. Aiming at preserving patients’ privacy and increasing the security of medical images in the cloud, this letter proposes a secure and privacy-preserving technique which provides a new security mechanism for medical data. In this technique, a novel reversible data hiding (RDH) based on adaptive texture classification is proposed to embed privacy data into medical images for preserving patients’ privacy and improving image quality, and plaintext encryption is proposed to encrypt the marked medical image into the similar image of target image for increasing image security. Extensive experiments have shown that the proposed RDH is better than other RDH methods. Plaintext encryption can reduce the attacker's attention and increase the security of medical images well.

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.

Temporal and spatial correlation-based reversible data hiding for RGB CFA videos

The paper mainly analyzes four typical reversible data hiding (RDH) algorithms that use PEE technology in combination: Conventional Prediction-error-expansion(C-PEE), Adaptive Prediction-error-expansion(A-PEE), Pairwise Prediction-error-expansion (P-PEE), Improve Pairwise Prediction-error-expansion(IP-PEE). And the experimental comparison results are also given. In addition, in the research process, we found that the peak signal-to-noise ratio (PSNR) and embedding capacity (EC) of many algorithms are very close, due to lack of benchmark tools for technical evaluation in the RDH field, thus this paper proposes a new performance measurement method based on the PSNR and EC measurement, we call it the prediction error accuracy rate, Determined by the ratio of the capacity that meets the embedding conditions to the total capacity of the image, combined with the measurement of this method, can better determine whether the performance of the algorithm is optimal. Finally, in this area, challenge and future directions are also given.

In this work, a reversible data hiding (RDH) algorithm is proposed for high dynamic range (HDR) images containing an additional luminance channel. Since prediction accuracy is the key of RDH, we propose to embed data into HDR images by exploiting the edge information among the luminance channel and color channels to achieve accurate prediction and high embedding capacity. Besides, a new edge-directed order is presented to reduce the visual loss of the stego image. Various experimental results demonstrate that the proposed algorithm can hide more data into HDR images with less distortion than directly applying traditional RDH methods designed for low dynamic range (LDR) images. Compared to the current HDR hiding algorithms, the proposed method is not only reversible, but also achieves a tradeoff between embedding capacity and distortion.

Reversible Data Hiding (RDH) techniques are becoming more important in the present era of digital content security. In RDH the reciprocal nature of embedding capacity and visual quality parameters, hinder further visual quality enhancement. The trade-off between embedding capacity and visual quality is an essential characteristic of any data hiding technique. Moreover, the problem of Random noise further deteriorates the visual quality of embedded content. Hence, addressing above problems in RDH techniques widely influence applications like remote sensing, medical diagnosis and satellite imagery. In this paper we are proposing a Laplacian Reversible Data Hiding technique, that perform 4-connected neighbor pixel difference expansion. This technique simultaneously improves image quality and embedding capacity by breaking reciprocal nature of embedding capacity and visual quality. Furthermore, it removes isolated edges and eliminate smoothing effect by sharpening the edges. Moreover, this can withstand from mathematical translation and rotation attack, so it can be called as semi-fragile data hiding algorithm.

With the advent of 3D video compression and Internet technology, 3D videos have been deployed worldwide. Data hiding is a part of watermarking technologies and has many capabilities. In this paper, we use 3D video as a cover medium for secret communication using a reversible data hiding (RDH) technology. RDH is advantageous, because the cover image can be completely recovered after extraction of the hidden data. Recently, Chung et al. introduced RDH for depth map using prediction-error expansion (PEE) and rhombus prediction for marking of 3D videos. The performance of Chung et al.’s method is efficient, but they did not find the way for developing pixel resources to maximize data capacity. In this paper, we will improve the performance of embedding capacity using PEE, inter-component prediction, and allowable pixel ranges. Inter-component prediction utilizes a strong correlation between the texture image and the depth map in MVD. Moreover, our proposed scheme provides an ability to control the quality of depth map by a simple formula. Experimental results demonstrate that the proposed method is more efficient than the existing RDH methods in terms of capacity.

Reversible data hiding (RDH) is crucial in modern data security, ensuring confidentiality and tamper-proofness in various industries like copyright protection, medical imaging, and digital forensics. As technology advances, RDH techniques become essential, but the trade-off between embedding capacity and visual quality must be heeded. In this paper, the relative correlation between the pixel's local complexity and its directional prediction error is employed to enhance an efficient RDH without using a location map. An embedding process based on multiple cumulative peak region localization (MCPRL) is proposed to hide information in the 3D-directional prediction error histogram with a lower local complexity value and avoid the underflow/overflow problems. The carrier image is divided into three color channels, and then each channel is split into two non-overlapping sets: blank and shadow. Two half-directional prediction errors (the blank set and the shadow set) are constructed to generate a full-directional prediction error for each color channel belonging to the host image. The local complexity value and directional prediction error are critical metrics in the proposed embedding process to improve security and robustness. By utilizing these metrics to construct a 3D stego-Blank Set, the 3D stego-shadow Set will be subsequently constructed using the 3D blank set. The proposed technique outperforms other state-of-the-art techniques in terms of embedding capacity, image quality, and robustness against attacks without an extra location map. The experimental results illustrate the effectiveness of the proposed method for various 3D RDH techniques.

Reversible data hiding (RDH) not only protects information but also extends its applications across various fields. This paper improves the Pixel Value Ordering (PVO) algorithm to enhance embedding capacity in dual images. We developed an optimized embedding method, allowing for higher data embedding performance compared to related works. Experimental results demonstrate that, relative to similar methods, this improvement enhances both embedding capacity and image quality. Keywords: Reversible data hiding; PVO; dual image