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As the number of people using the Internet has increased, more information is stored and accessible daily. As a result, the requirement for information security also grows. In the early stages of data security, cryptography is used. Cryptography turns readable information into an unreadable form. Steganography is the next generation of information security. The main downside of this steganography is that the digital media becomes damaged due to hiding information in digital media. The next stage of information security is Reversible Data Hiding (RDH). This method can restore personal information and digital media without error. The next method, Separable Reversible Data Hiding in Encrypted Digital Media, recovers the digital media and extracts concealed information independently without disturbing or knowing each other. This paper presents a novel Separable Reversible Data Hiding by Vacating Room After Encryption using the Encrypted Pixel Difference (SRDH-VRAE-EPD) method, which combines homomorphic encryption and encrypted pixel differences. The proposed method offers the following advantages. It achieves an embedding rate of 1.2 bpp, significantly improving upon standard VRAE algorithms while allowing for lossless data extraction and image recovery. The encrypted image ensures high security against various attacks, including statistical, differential, and chosen plaintext attacks, and it allows for the extraction of secret data and recovery of the original image independently, making it a separable process.

In this modern era, a large amount of multimedia content plays an important role in various fields. For multimedia content, storage space and processing speed are more crucial. As a result, existing multimedia applications are moving to a cloud-based paradigm since it offers greater storage and faster processing capabilities. This ensures that more and more people choose to save and process their multimedia content on the cloud. However, this option might cause severe repercussions due to inadequate security. Homomorphic encryption is a type of encryption that enables users to do computations on encrypted data without having to decrypt it first. These resulting operations are then stored in an encrypted form, which when decrypted, produces the same outcomes as if the operations had been performed on the unencrypted data. This paper aims to present a promising solution to protect the data on the cloud through Reversible Data Hiding in an Encrypted Image (RDHEI), using homomorphic encryption and a rhombus pattern prediction scheme. Using this proposed method, any third party can perform data-hiding operations on an encrypted image without being aware of the original contents. Furthermore, this method has the advantage of protecting the image very securely. The entropy of the encrypted image is 7.999, deviations from ideality are 0.0245, diagonal correlation and vertical correlation are 0.0092 and − 0.0015, respectively, and embedding capacity is 0.498 bpp. Finally, flawless image recovery and covert extraction are possible.

The volatile development in the multimedia cognitive content is changing the global set-up towards a cloud-based architecture which is helped us with a massive amount of computer storage and the highest computational platform. Cost-saving and elasticity of services will be provided by progressive cloud computing technology for users. With the advancement in multimedia technology, the data owners outsource their private multimedia data on the hybrid cloud. Meantime the cloud servers also carry out some highly computationally expensive tasks. Nevertheless, there is an opportunity for security infracts possible in the public cloud environment. It makes an alarm for a cloud environment in security aspects. Before outsourcing multimedia data, an encryption technique is needed for safeguarding against several attacks. But performing the same is a significant challenge. A new research area was recently awakened on privacy-preserving Reversible Data Hiding (RDH) especially for multimedia data over the outsourced environment. A novel RDH for an encrypted image was proposed in this paper by using the (Most Significant Bit) MSB difference of the pixel value. By using this method, any third-party people can embed the ciphertext in the cipher image without the knowledge of the cover and secret. A person with decryption keys can get back the secret and the cover without any loss. The proposed work achieves the embedding capacity up to 1 bpp (bits per pixel) with the encryption quality of near-zero correlation and uniform histogram. The decrypted image is also retrieved with infinite Peak Signal to Noise Ratio (PSNR), unit Structural Similarity Index Metric (SSIM) and zero Bit Error Rate (BER).

Data hiding in digital images is an important cover communication technique. This paper studies the lossless data hiding in an image compression domain. We present a novel lossless data hiding scheme in vector quantization (VQ) compressed images using adaptive prediction difference coding. A modified adaptive index rearrangement (AIR) is presented to rearrange a codebook, and thus to enhance the correlation of the adjacent indices in the index tables of cover images. Then, a predictor based on the improved median edge detection is used to predict the indices by retaining the first index. The prediction differences are calculated using the exclusive OR (XOR) operation, and the vacancy capacity of each prediction difference type is evaluated. An adaptive prediction difference coding method based on the vacancy capacities of the prediction difference types is presented to encode the prediction difference table. Therefore, the original index table is compressed, and the secret data are embedded into the vacated room. The experimental results demonstrate that the proposed scheme can reduce the pure compression rate compared with the related works.

In this paper, a robust lossless digital watermarking scheme based on a generalized integer transform in spatial domain is proposed. In the proposed method, data bits are hidden into the cover media by a reversible generalized integer transform with bit plane manipulation. With the reversible transform, data can be hidden in the cover media, and the stego media can be restored to its original form after extraction of the hidden data. In embedding procedure, adaptive bit plane manipulation is applied to increase robustness of the algorithm while keeps good visual quality. To further increase the robustness of the algorithm, we repeatedly embed watermark bits and use majority voting to decode the hidden information in extraction procedure. Furthermore, a threshold is introduced in the algorithm, which helps in choosing regions that would result lower variance for embedding, as regions with lower variance is more robust against JPEG compression. The proposed scheme is quite different from the existing robust lossless data hiding algorithms which are histogram-based. The performance of the proposed scheme is evaluated and compared with state-of-the-arts techniques respect to robustness, data payload capacity and peak signal-to-noise ratio (PSNR). In the experiments, the proposed method can embed more than 10000 bits into 512 by 512 grayscale and medical images, and has around 30 dB in PSNR. In case of small watermark with 100 bits, marked images can have PSNR above 60 dB and with 0.1 bpp in JPEG robustness in the best cases. Conclusively, the robustness of the proposed method is quite good, and the results of hiding capacity and imperceptibility are also satisfactory.

This article reports on a lossless data hiding scheme for digital images where the data hiding capacity is either determined by minimum acceptable subjective quality or by the demanded capacity. In the proposed method data is hidden within the image prediction errors, where the most well-known prediction algorithms such as the median edge detector (MED), gradient adjacent prediction (GAP) and Jiang prediction are tested for this purpose. In this method, first the histogram of the prediction errors of images are computed and then based on the required capacity or desired image quality, the prediction error values of frequencies larger than this capacity are shifted. The empty space created by such a shift is used for embedding the data. Experimental results show distinct superiority of the image prediction error histogram over the conventional image histogram itself, due to much narrower spectrum of the former over the latter. We have also devised an adaptive method for hiding data, where subjective quality is traded for data hiding capacity. Here the positive and negative error values are chosen such that the sum of their frequencies on the histogram is just above the given capacity or above a certain quality.

This paper presents a reversible data hiding scheme. The proposed scheme is based on the pixel difference histogram shifting to spare space for data hiding. Pixel differences are generated between a center pixel and its neighbors in a pre-assigned block. A large number of pixel differences with equal values can be obtained which means more data can be embedded into the cover image than previous works based on histogram shifting. In addition, multi-layer embedding with various turns is used to increase the hiding capacity, and in each turn, we can offer high embedding capacity and keep low distortion by choosing the optimized block size and the best threshold. Experiments have been conducted to show the effectiveness of the proposed method. Furthermore, the proposed scheme can be implemented easily and no extra information except message length is needed to extract data and restore image. Index Terms-Reversible data hiding, Lossless data hiding, Histogram shifting, Center pixel difference, Multi-layer embedding

One of the open problems in lossless information hiding research is how to get adaptively better difference image architectures for given applications. In this paper we propose a simple and efficient approach to predict high-similar interpolation image from its sparse pattern and spectral expansion. After difference operator, peak value of the spike is very high. This method also provides a mathematic framework for evaluating de-correlating algorithm and can therefore be used to benchmark new algorithms. Finally, a proper reversible data hiding algorithm is also enclosed which refers conventional difference expansion principle. Overflow and underflow is considered in the fusion way. Simulations results demonstrate and verify that our new approach is much effective than local difference expansion method with good generalization performance.

In this paper, we propose a new framework for reversible data hiding in encrypted images, where both the hiding capacity and lossless compression efficiency are flexibly controlled. There exist two main purposes; one is to provide highly efficient lossless compression under a required hiding capacity, while the other is to enable us to extract an embedded payload from a decrypted image. The proposed method can decrypt marked encrypted images without data extraction and derive marked images. An original image is arbitrarily divided into two regions. Two different methods for reversible data hiding in encrypted images (RDH-EI) are used in our method, and each one is used for either region. Consequently, one region can be decrypted without data extraction and also losslessly compressed using image coding standards even after the processing. The other region possesses a significantly high hiding rate, around 1 bpp. Experimental results show the effectiveness of the proposed method in terms of hiding capacity and lossless compression efficiency.

The goal of this survey is to review the state-of-the art Reversible Data Hiding (RDH) methods, classify these methods into different classes, and list out new trends in this field. RDH, in general, is a challenging problem and has potential applications in the today’s digital world. Reversible data hiding methods not only securely transfer secret data but also recover the cover media faithfully. Recently, RDH methods are mainly focused on obtaining high capacity along with tuneable quality. Although, extensive investigations in the field of reversible data hiding was carried out in the recent past, a comprehensive review of existing literature for listing out research gap and future directions has not yet been reported. In this survey, we have classified the reversible data hiding methods mainly into a) Plain domain b) Encrypted domain and also examine their pro and cons. Tabular comparison of various RDH methods has been provided considering various design and analysis aspects. Moreover, we discuss important issues related to reversible data hiding and use of benchmarked datasets along with performance metrics for evaluation of RDH methods.