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In the field of reversible data hiding (RDH), how to predict an image and embed a message into the image with smaller distortion are two important aspects. In this paper, we propose a novel and efficient RDH method by innovating an intelligent predictor and an adaptive embedding way. In the prediction stage, we first constructed a convolutional neural network (CNN) based predictor by reasonably dividing an image into four parts. In such a way, each part can be predicted by using the other three parts as the context for the improvement of the prediction performance. Compared with existing predictors, the proposed CNN predictor can use more neighboring pixels for the prediction by exploiting its multi-receptive fields and global optimization capacities. In the embedding stage, we also developed a prediction-error-ordering (PEO) based adaptive embedding strategy, which can better adapt image content and thus efficiently reduce the embedding distortion by elaborately and luminously applying background complexity to select and pair those smaller prediction errors for data hiding. With the proposed CNN prediction and embedding ways, the RDH method presented in this paper provides satisfactory results in improving the visual quality of data hidden images, e.g., the average PSNR value for the Kodak benchmark dataset can reach as high as 63.59 dB with an embedding capacity of 10,000 bits. Extensive experimental results have shown that the RDH method proposed in this paper is superior to those existing state-of-the-art works.

In this paper, we propose an improvement in the preprocessing stage of the reversible data hiding method based on contrast enhancement applied to grayscale digital images. In conventional proposals, the preprocessing stage plays a fundamental role in the performance of the data hiding algorithm in terms of capacity and visual quality. Thus, this work presents two improvements to the preprocessing stage based on the analysis, merging, and shifting the image histogram, which, in conjunction with the data hiding stage, maximize the embedding capacity while keeping the visual distortion at a minimum. The proposed algorithm was implemented on two image datasets and compared with several related works previously reported in the scientific literature. The experimental results show that the visual quality can be preserved after data have been concealed when employing different embedding rates. Either of the preprocessing techniques presented in this paper can be adopted considering the application scenario and the priority of the requirements of the capacity and visual quality needed.

Fusing the single modality medical images to obtain a distinct multimodality image is needed to ensure a better clinical experience. However, the distribution of these fused images brings the issues of ownership and authentication and has attracted many researchers. Presently, large volumes of medical data are stored on cloud platforms. However, outsourcing medical data to this popular platform may introduce security issues. Following this, we introduce a secure data hiding in fused medical image for smart healthcare, since it is also suitable for applications in the cloud. To achieve this, we first create a fused medical image as a cover by nonsubsampled contourlet transform (NSCT). The method, which is based on NSCT, QR, and Schur decomposition, allows concealing the image and electronic patient records (EPR) mark into the fused image. Importantly, EPR watermark includes a hash value of cover is created first and then embedded into the cover via magic cube-based procedure. Finally, the marked image is encrypted using deoxyribonucleic acid (DNA), chaotic maps, and a hash function-based encryption scheme. The introduced watermarking scheme has been evaluated using 25 pairs of medical images and several embedding/extracting parameters. Apart from being satisfactorily imperceptible, the proposed work is also robust and secure against well-known signal processing attacks and promising results are obtained when compared with similar techniques. It indicates a considerable improvement in robustness of 66.7% and 99.7% over existing discrete wavelet transform (DWT)-singular value decomposition (SVD)-based watermarking schemes.

The emergence of data hiding schemes was to provide multimedia security besides the sharing of information in a confidential manner. Since then, various techniques have cropped up to meet the objective of secured data communication using multimedia documents. In this paper, a novel data hiding approach has been designed to exploit the superpixel of the image to identify the nature of the blocks for embedding data using spatial and transform domain techniques such as Cellular Automata (CA) and Discrete Cosine Transform (DCT) respectively. In addition, an Arnold transform is used to provide a blanket of security through a random selection of blocks via sharable keys. The proposed technique obtains YCbCr color model of an RGB image to use superpixel on only the Y color component. Thereafter, the acquired labeled image of superpixel is partitioned into ( 8 × 8 ) blocks which are to be categorised as homogeneous and heterogeneous blocks. Based on the categorization, DCT and CA are applied for the embedding of the secret data in the Cb and Cr color components. The experimental results and its comparison with state-of-the-art schemes uphold the effectiveness of the demonstrated scheme. Various analysis have been conducted to showcase the degree of robustness, imperceptibility, and visual quality attained by the proposed scheme.

Image data play an important role in the network information, however, some images containing sensitive or confidential information are easy to attract the attention of malicious attackers. Based on deep learning and data hiding technology, a novel hiding-revealing network is designed to protect these secret images. The sender uses the hiding network to conceal the secret image into an ordinary cover image and the receiver uses the revealing network to recover the secret image. Symmetrical shortcut connection is designed to improve both the hiding and the revealing performance without adding any parameters. Consider that some secret images may have complex spatial features, a multi-level strong auxiliary module is designed to enhance feature representation and boost the restoration quality of the secret image. Then, a lifeline is proposed to transform the image hiding task into a residual identity mapping, which reduces the difficulty of network learning and obviously improves the hiding performance. In addition, a mixed loss function is designed to further improve the perceptual quality of both the hidden image and the revealed image, which further completely eliminates the secret content in the residual image and ensures the hiding security. Experimental results demonstrate that, compared with the state-of-the-art methods, our proposed method achieves the best performance in both hidden image synthesis and secret image restoration.

To securely transmit secret data between IoT nodes, it is required to implement information hiding technique for secure communication in IoT environment. The traditional information hiding approaches generally select a multimedia file such as texts, images and video clips as the cover, and then embed secret information into the cover by slight modification. However, it is not feasible to directly apply these approaches in IoT environment for the following reasons. First, it is hard for some IoT nodes to effectively and efficiently process and transmit the complex multimedia data. Second, the modification trace left in the cover will cause the presence of hidden secret information to be easily exposed by steganalysis tools. To address the above issues, we propose a coverless information hiding scheme based on probability graph learning for secure communication in IoT environment. Instead of modifying an existing multimedia cover, we conceal secret information in a generated sequence of Internet-of-Things (IoT) data to realize secure communication between different nodes in the IoT environment. According to the node-data interaction relationships, we first learn the transition probability graph (TPG) to describe the transition probabilities between IoT data elements. Then, guided by a given secret message that needs to be hidden, we sequentially select a set of highly-correlated data elements from the TPG to generate the sequence. The experimental results and theoretical analysis demonstrate that the proposed information hiding scheme can achieve high hiding capacity with desirable imperceptibility and security performances in the IoT environment.

Recently, most coverless image steganography (CIS) methods are based on robust mapping rules. However, due to the limited mapping expression relationship between secret information and hash sequence, it is a challenge to further improve the hiding ability of coverless information hiding. Towards this goal, this paper proposes a robust coverless steganography scheme using camouflage image(CI-CIS). For the sender, CI-CIS introduces an camouflage image as the transmission carrier and establishes the correlation between them by Convolutional Neural Network(CNN) features. For the receiver, the camouflage image can retrive the corresponding stego-image to recover the secret information. To this end, we designed a reversible retrieval scheme between stego-image and camouflage image by using image clustering. At the same time, since the semantic features represented by CNN are robust to image attacks, our method can increase the capability of the CIS effectively. Besides, we also build an inverted index to improve retrieval efficiency. Experimental results and analysis show that the CI-CIS has higher robustness and more flexible capacity setting compared with the existing CIS methods.

With the advancement of technology, the maximum data hiding capacity and security of cover objects have become a very challenging task for researchers, particularly in text carrier. Text carrier depicts low hiding capacity but more secure for the detection of confidential information. It demands novelty in data hiding algorithms. In this regard, a novel algorithm is proposed by using steganography and cryptography together for the enhancement of capacity and security of confidential data. The recommended algorithm uses a linguistic steganography method to conceal data into the Arabic text carrier. In the described algorithm, the identification of secret information from text files is hard due to less redundant bits in the text as compared to the image, audio, and video steganographic mediums. The current solution uses Unicode characters such as Zero-Width-Character (ZWC) and Zero-Width-Joiner (ZWJ) to hide the secret information. Before hiding confidential information, secret data is encrypted by using bit inversion due to which algorithm achieved high security. It is observed from the simulation results that the proposed algorithm successfully achieved high cover medium capacity, security, and robustness.

Secret sharing is a useful method which divides a secret message into several shares for security. During the recovery procedure, only when sufficient shares are obtained, the secret message can be recovered. This paper proposes two novel reversible data hiding schemes in encrypted image via secret sharing over Galois fields GF(p) and GF(28). The content owner first applies a specific encryption method through block and pixel permutation and Shamir’s secret sharing. Then, the theoretical demonstration is introduced to explain that the generated shares are suitable for data embedding over GF(p) and GF(28). Finally, two embedding algorithms over GF(p) and GF(28) are presented, and on the receiver side, with different keys, additional data can be extracted correctly and original image can be recovered losslessly. Experimental results show that our schemes can achieve better rate-distortion performance than some state-of-the-art schemes.

The rapid development of information technology has brought security to be a crucial factor. Information being transferred between different parties must be well protected to prevent the file from illegal access. Data hiding, as one of the possible securing methods, has been introduced; nevertheless, the quality of the stego file and the capacity of the secret are still challenging. In this research, we work on these problems by exploring the histogram developed in the frame and the prediction error generated between two consecutive frames of a video file. In the generated stego file, these updated prediction errors do not change the composition and the total number of frames. The experimental results show that this proposed method has achieved a better result than the compared algorithms, which is shown by the Peak Signal to Noise Ratio (PSNR) value for various capacities. Equivalently, the number of bits that can be held is higher than others for the same stego quality. Besides, this method is reversible, in which both the cover and the secret can be fully extracted from the stego file. It means this scheme is appropriate to implement in a system, which requires the existence of those both data.

Block permutation and Co-XOR (BPCX) image encryption is a commonly used encryption method for reversible data hiding in the encryption domain, which can effectively improve the embedded capacity and the ability resisting the existing attacks including ciphertext-only attack and known plaintext attack (KPA). This paper proposes a KPA based on bit-block inversion and mean equivalent division (MED) to estimate the block permutation key of BPCX image encryption. Firstly, we divide an image block into the bit-block and point out that the maximum of the numbers of 0 bit and 1 bit of a bit-block before and after the Co-XOR encryption remains unchanged. And then two inversion rules of bit-block are defined to construct pseudo plain-ciphertext images to eliminate the influence of pixel value changes caused by Co-XOR encryption. Finally, the MED based KPA is designed to estimate the block permutation key sequence according to the pseudo plain-ciphertext images. The relationship between the key estimation accuracy and the number of known plain-ciphertext pairs, block size, and pseudo ciphertext are discussed. Experimental results show that even in the minimum block size (2×2), the average estimated correct rate of the block permutation sequence exceeds 40%. The block permutation key estimation accuracy is more than 50% when the block size is greater than 3×3. Some improved encryption methods against the proposed KPA are also given.

Reversible data hiding in encrypted images (RDHEI) is an effective technique of data security. Most state-of-the-art RDHEI methods do not achieve desirable payload yet. To address this problem, we propose a new RDHEI method with hierarchical embedding. Our contributions are twofold. (1) A novel technique of hierarchical label map generation is proposed for the bit-planes of plaintext image. The hierarchical label map is calculated by using prediction technique, and it is compressed and embedded into the encrypted image. (2) Hierarchical embedding is designed to achieve a high embedding payload. This embedding technique hierarchically divides prediction errors into three kinds: small-magnitude, medium-magnitude, and large-magnitude, which are marked by different labels. Different from the conventional techniques, pixels with small-magnitude/large-magnitude prediction errors are both used to accommodate secret bits in the hierarchical embedding technique, and therefore contribute a high embedding payload. Experiments on two standard datasets are discussed to validate the proposed RDHEI method. The results demonstrate that the proposed RDHEI method outperforms some state-of-the-art RDHEI methods in payload. The average payloads of the proposed RDHEI method are 3.4568 bpp and 3.6823 bpp for BOWS-2 dataset and BOSSbase dataset, respectively.

Vacating room after encryption (VRAE) is a popular framework of reversible data hiding for encrypted images (RDHEI). Most VRAE based RDHEI methods do not make a desirable payload. To address this issue, this paper proposes a novel data hiding technique using adaptive difference recovery (ADR) and exploits this novel technique to design an efficient VRAE based RDHEI method. Specifically, the proposed ADR-based data hiding technique adaptively determines the range of original difference between a cover pixel and a reference pixel with correlation according to the marked difference generated by bit substitution technique. The cover pixel can be recovered losslessly with the range of original difference and the reference pixel. In the proposed RDHEI method, the encrypted image is generated by block permutation and block-based modulation so that spatial redundancy within encrypted image blocks is preserved. Then the ADR-based data hiding technique is applied to the encrypted blocks. Experiment results show that the proposed RDHEI method outperforms some state-of-the-art RDHEI methods in payload. The average embedding rates of the proposed RDHEI method on the BOSSbase, BOWS-2 and UCID datasets are 3.2045, 3.1145 and 2.4633 bpp, respectively.

Reversible data hiding in encrypted images (RDH-EI) technology is commonly used in cloud storage images for privacy protection. Most existing RDH-EI techniques reported in the literature applied block permutation and co-modulation (BPCM) encryption to generate encrypted images. This work analyses the security of the RDH-EI algorithm based on BPCM encryption under known plaintext attack (KPA). Different from the existing KPAs, this paper considers that attackers can perform KPA based on marked encrypted images and shows that BPCM encryption has the risk of information leakage. To find the constant features of a block before and after co-modulation, the first-pixel difference block (FDB) of a block is first defined. Then, a pseudo cypher difference image of the cyphertext image is constructed to eliminate the changed FDBs so that the differences in the cyphertext FDBs are the same as the FDBs in the corresponding plaintext difference image. Finally, we design an FDB-based block permutation key estimation method according to the plaintext difference image and pseudo cyphertext difference image. The influence of block size on key estimation accuracy and the time complexity of the proposed KPA algorithm are analysed and discussed. Experimental results show that the correct rate of key estimation is positively correlated with the block size and the number of plain-cyphertext pairs. The average correct rate of key estimation reaches 63% when the block size is greater than 33.

Reversible data hiding in encrypted image (RDHEI) is a technique that can be adopted by cloud sever to embed additional data into the encrypted image with no permanent distortion. For RDHEI, it remains a challenging task to improve the embedding capacity under the premise of real reversibility. In this paper, a novel RDHEI method based on adaptive most significant bit (MSB) prediction is proposed. The cover image is first encrypted in block-wise manner such that the correlation of pixels within block is preserved. After that, all blocks are permuted to fulfill the final encryption. During data embedding, the upper-left pixel within block is used to predict others such that the embedding room is vacated. Then, available blocks are selected and all blocks are rearranged so as to ensure reversibility. By fully exploiting the correlation of pixels within block via adaptive MSB prediction, the proposed method successes to achieve desirable improvement in capacity. Experimental results show that the proposed method significantly outperforms previous methods. Moreover, real reversibility and real separability are also guaranteed. In a word, the proposed method is a practical method that can be adopted by cloud storage

This paper addresses the issue of detecting the use of information hiding at anti-copying 2D barcodes. Prior hidden information detection schemes have their roots in either heuristic-based or Machine Learning (ML). However, prior heuristics-based schemes lack a rigorous theoretical analysis. Prior ML-based information schemes lack robustness because a printed 2D barcode is very much environmentally dependent. Thus, an information hiding detection scheme trained in one environment often does not work well in another environment. In this paper, we propose two hidden information detection schemes for existing anti-copying 2D barcodes. The first scheme directly uses the pixel distance to detect the use of an information hiding scheme in a 2D barcode, referred to as the Pixel Distance Based Detection (PDBD) scheme. The second scheme first calculates the variance of raw signal and the covariance between the recovered and raw signals, and then based on the variance results, detects the use of information hiding scheme in a 2D barcode, referred to as the Pixel Variance Based Detection (PVBD) scheme. Moreover, we design advanced Illegitimately-Copying (IC) attacks to evaluate the security of two existing anti-copying 2D barcodes. We conduct extensive performance comparisons among the proposed schemes and prior schemes under different capturing devices, e.g., different scanners or camera phones. Our experimental results show that the PVBD scheme can correctly detect the existence of hidden information at both the 2LQR code and the LCAC 2D barcode. Moreover, the successful attacking probability of the proposed IC attacks achieves 0.6538 for the 2LQR code and 1 for the LCAC 2D barcode.

Digital images are easily corrupted by attacks during transmission and most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, we propose a robust image data hiding method based on multiple backups and pixel bit weight (PBW). Especially multiple backups of every pixel bit are pre-embedded into a cover image according to a reference matrix. Since different pixel bits have different weights, the most significant bits (MSBs) occupy more weights on the secret image than those of the least significant bits (LSBs). Accordingly, some backups of LSBs are substituted by the MSBs to increase the backups of MSBs so that the quality of the extracted secret image can be improved. Experimental results show that the proposed algorithm is robust to cropping and noise attacks for secret image.

In recent years, high-dimensional histogram modification based reversible data hiding for images has drawn much attention among researchers. Pairwise prediction-error expansion (pairwise PEE) achieves great performance by exploiting the correlations among prediction-errors, in which a two-dimensional (2D) prediction-error histogram (PEH) is generated, and then modified based on a specific 2D modification mapping for reversible embedding. However, the used 2D mapping is fixed and heuristically designed without considering the image content, so the embedding performance of pairwise PEE can be further improved. To this end, the adaptive pairwise PEE (APPEE) is proposed in this paper to adaptively design the 2D mapping according to the distribution of 2D PEH, such that a better embedding performance is derived. To further improve the embedding performance, the proposed APPEE is extended from one single 2D PEH to multiple 2D PEHs generated based on different local complexities, in which multiple content-based 2D mappings are designed adaptively and individually for each 2D PEH. Experimental results show that the APPEE for either one single 2D PEH or multiple 2D PEHs significantly outperforms the conventional pairwise PEE. Simultaneously, the superiority of the proposed method is also experimentally verified compared with some other state-of-the-art works.

PurposeSteganography is a data hiding technique used in the data security. while transmission of data through channel, no guarantee that the data is transmitted safely or not. Variety of data security techniques exists such as patch work, low bit rate data hiding, lossy compression etc. This paper aims to increase the security and robustness.Design/methodology/approachThis paper describes, an approach for multiple images steganography that is oriented on the combination of lifting wavelet transform (LWT) and discrete cosine transform (DCT). Here, we have one cover image and two secret images. The cover image is applied with one of the different noises like Gaussian, Salt & Pepper, Poisson, and speckle noises and converted into different color spaces of YCbCr, HSV, and Lab.FindingsDue to the vast development of Internet access and multimedia technology, it becomes very simple to hack and trace secret information. Using this steganography process in reversible data hiding (RDH) helps to prevent secret information.Originality/valueWe can divide the color space converted image into four sub-bands of images by using lifting wavelet transform. By selecting lower bands, the discrete cosine transform is computed for hiding two secret images into the cover image and again one of the transformed secret images is converted by using Arnold transform to get the encrypted/embedded/encoded image. To extract the Stego image, we can apply the revertible operation. For comparing the results, we can calculate PSNR, SSIM, and MSE values by applying the same process for all color spaces of YCbCr, HSV, and Lab. The experimental results give better performance when compared to all other spaces.

Reversible Data Hiding (RDH) schemes have recently gained much interest in protecting the secret information and sensitive cover images. For cloud security applications, the third party’s data embedding can be done (e.g., cloud service). In such a scenario, to protect the cover image from unauthorized access, it is essential to encrypt before embedding it. It can be overcome by combining the RDH scheme with encryption. However, the key challenge in integrating RDH with encryption is that the correlation between adjacent pixels begins to disappear after encryption, so reversibility cannot be accomplished. RDH with elliptic curve cryptography is proposed to overcome this challenge. In this paper (ECC-RDH) by adopting additive homomorphism property; the proposed method, the stego image decryption gives the sum of the original image and confidential data. The significant advantages of this method are, the cover image is transferred with high security, the embedding capacity is 0.5 bpp with a smaller location map size of 0.05 bpp. The recovered image and secrets are the same as in the original, and thus 100% reversibility is proved.

Digital steganography, the modern version of the ancient art of data hiding is a prevalent tool for covert communication. Steganalysis, at least as old as steganography comes handy to unearth such hidden channels. The illegal act of information hiding through steganography of digital images can be overcome effectively only by using intelligent steganalytic techniques. In this paper, a novel MixNet framework comprising of six Convolutional Neural Networks (CNNs) is proposed as feature extractors for accomplishing generic steganalysis of spatial content-adaptive algorithms with better detection accuracy. Since the spatial content-adaptive algorithms embed secret bits in the hard to model components of the image like edges or textures, inputs to the CNNs are initially filtered using high pass filters to obtain the embedded content in the form of noise residual. Hierarchical features extracted from these networks are then concatenated and used to train Support Vector Machine classifier. Experimentation is performed using the benchmark BOSSbase v1.01 cover images and stego images are created with three state-of-the-art algorithms HUGO-BD, S-UNIWARD and WOW at five relative payloads 0.1–0.5 bits per pixel (bpp). The experimental results show that the proposed MixNet outperforms the compared related works in literature and proves the robustness of MixNet in detecting content-adaptive steganography.

Our world is making its way toward complete digitization, with more and more data going to the Internet. Thus, the security of the data has become one of our utmost responsibilities. Strong and sec...

In this paper, a novel separable reversible data hiding in homomorphic encrypted images (RDHEI) using POB number system is proposed. The frame of the proposed RDHEI includes three parties: content owner, data hider, and receiver. The content owner divides original image contents into a series of non-overlapping equal-size 2 × 2 blocks, and encrypts all pixels in each block with the same key. The encryption process is carried out in an additive homomorphism manner. The data hider divides the encrypted images into the same size blocks as the encryption phase, and further categories all of the obtained blocks into two sets according to the corresponding block entropy. The embedding processes of the two sets are performed through utilizing permutation ordered binary (POB) number system. For the set with smaller entropies, all pixels in addition to the first pixel in each block are compressed by the POB number system; for the set with larger entropies, only u LSBs of all pixels are compressed in order to vacate room for embedding. The receiver can conduct image decryption, data extraction, and image reconstruction in a separable manner. Experimental results verify the superiority of the proposed method.

In recent years, we face an increasing interest in protecting multimedia data and copyrights due to the high exchange of information. Attackers are trying to get confidential information from various sources, which brings the importance of securing the data. Many researchers implemented techniques to hide secret information to maintain the integrity and privacy of data. In order to protect confidential data, histogram-based reversible data hiding with other cryptographic algorithms are widely used. Therefore, in the proposed work, a robust method for securing digital video is suggested. We implemented histogram bit shifting based reversible data hiding by embedding the encrypted watermark in featured video frames. Histogram bit shifting is used for hiding highly secured watermarks so that security for the watermark symbol is also being achieved. The novelty of the work is that only based on the quality threshold a few unique frames are selected, which holds the encrypted watermark symbol. The optimal value for this threshold is obtained using the Firefly Algorithm. The proposed method is capable of hiding high-capacity data in the video signal. The experimental result shows the higher capacity and video quality compared to other reversible data hiding techniques. The recovered watermark provides better identity identification against various attacks. A high value of PSNR and a low value of BER and MSE is reported from the results.

A definition of finite time synchronization of chaotic system was proposed, and a special theorem was proposed to solve the difficult problem of constructing a finite time stable system. After that, a hybrid construction method was proposed by integrating a common stable system and a finite time stable system. That reveals how to construct a finite time stable system, and it is very useful in secure communication since the convergence time is a very important factor that will affect its application in engineering realization. Above theorem and method was applied in the chaotic synchronization and two kinds of synchronization methods were proposed with estimation of unknown parameters. At last, a secure communication scheme was constructed by using above finite time synchronous method of chaotic systems. Also, numerical simulation was done, and the rightness of all the above proposed theorems and methods was shown.

Nowadays, Internet has become essential for living and running businesses smoothly; it has made things simpler like online transaction, online shopping, sharing images, videos, audios, messages on social media, uploading some important information on Google drives, etc. So the very first requirement is to secure and protect digital contents from any unauthorized access. Reversible Data Hiding (RDH) is one of the ways to provide security in digital content, through which useful information can be embedded in the digital content, and at the receiver’s end, the complete recovery of cover media as well as the embedded message is possible. In this digital era, digital images are most rapidly used for communication purposes; therefore, the security of digital images is in high demand. RDH in digital images has gained a lot of interest during the last few decades. This paper describes and investigates a systematic review on various RDH techniques for digital images, which can be broadly classified into five categories: Lossless Compression Based, Histogram Modification Based, Difference Expansion Based, Interpolation Based and Encrypted image Based techniques.

The science of information security has become a concern of many researchers, whose efforts are trying to come up with solutions and technologies that ensure the transfer of information in a more secure manner through the network, especially the Internet, without any penetration of that information, given the risk of digital data being sent between the two parties through an insecure channel. This paper includes two data protection techniques. The first technique is cryptography by using Menezes Vanstone elliptic curve ciphering system, which depends on public key technologies. Then, the encoded data is randomly included in the frame, depending on the seed used. The experimental results, using a PSNR within average of 65 and MSE within average of 85, indicate that the proposed method has proven successful in its ability to efficiently embedding data.

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.

The increasing use of multimedia applications often makes the information prone to leakage and makes the user's privacy at risk. The information hiding technique is mainly used to secure multimedia communication/information by modifying the secret information in a way that makes it useless for an unintended user. In this way, the secret information is secured from unauthorized access. To create a mosaic image, initially, a style transfer is applied using the Convolutional Neural Network (CNN) architecture. The style transfer alters the style of an image using another image still preserving the original content. The CNN’S optimization problem is to optimize the content loss, style loss, and total variation loss done using an Archimedes Optimization Algorithm (AOA). The AOA algorithm also alters the block in the target image by selecting an appropriate tile in the secret image. The tile fitting information is randomly inserted into the selected pixels using an improved dual image-based LSB matching (DILSBM) scheme. The DILSBM scheme can retrieve both the secret and target images from the mosaic image by offering a high PSNR value. The proposed DILSBM and CNN optimized AOA architecture is primarily intended to provide high capacity, improved visual quality, and solve the source image selection problem. The feasibility of the proposed technique can be observed from the experimental analysis where it is analyzed using different performance metrics such as Embedding Rate, Structural Similarity Method (SSIM), and peak signal-to-noise ratio (PSNR).

E-healthcare framework requires transfer of patient report to a specialist in a real time framework. Subsequently, any damage to patient information can prompt a wrong diagnosis that can be deadly for the patient. To guarantee a secure communication in E-healthcare framework, a high capacity dual image reversible data hiding algorithm has been presented in this paper. Highlight of proposed method is to embed $$\lfloor \log _{2}(2 \times n+1) \times NZ -1 \rfloor$$ binary bits of patient information whereas no overflow problem has occurred during embedding process. For authentication analysis of electronic patient information (EPI) at recipient end, a fragile watermark has also been embedded with EPI respectively. To prove the effectiveness of proposed method, experiments have been performed on different test images. The average embedding rate of 2.36 bpp for all test images which demonstrates that the method is capable for embedding high payload in comparison of others respectively.

In this study, a new high-capacity, low-distortion, and reversible data hiding method based on RGB that hides color images inside color images is proposed. The proposed method divides the 24-bit image into 8-bit (1byte) meaningful segments, and the first most important 4 bits of the 8-bit data are hidden directly while the less important negligible 4-bit is not hidden and it is completed by rounding at approximate value through the function. The first 4 bits hidden in extraction are combined with the 4 bits from the function to retrieve 1 byte of information. In this way, since the 8-bit data is reduced to 4 bits, the method performs lossy hiding. Since the size of the data reduces by half owing to this method, it offers twice the capacity compared to the traditional LSB methods. When the same amount of data is hidden, the method creates lower distortion in the cover image than traditional LSB methods. Peak signal to noise ratio (PSNR) and structural similarity quality criterion (SSIM), which are frequently used in the literature, are used to measure the image quality of the proposed method. The test results showed that the proposed method achieved twice as much capacity in comparison to the traditional LSB method, higher efficiency than the studies in the literature, and higher PSNR and SSIM values as undetectable are obtained.

This manuscript proposes a new data hiding approach that is used in watermark applications in video by transforming the RGB model to HSV model. This method initially estimates the number of frames needed to embed the data (watermark). Then two sets of RGB (Red, Green, Blue) coefficients (R1, G1, B1), (R2, G2, B2) are converted to HSV (Hue, saturation, values) Coefficients (H1, S1, V1) and (H2, S2, V2). The ‘Value’ Coefficients V1 and V2 are used to embed the watermark, since there exists a strong correlation between the adjacent ‘Value’ Coefficients. The same process is repeated on adjacent HSV coefficients till the watermark is fully embedded. After embedding the data HSV coefficients are again converted back to RGB coefficients. During the extraction phase, the data is extracted by transforming the RGB coefficient to HSV coefficients. One bit of information can be extracted from two adjacent HSV coefficients. Experimental outcomes show that the proposed watermarking approach is efficiently against attacks, viz noise, filtering, etc. Also, the proposed method performs better than traditional watermarking methods with the help of embedding rate (bpp), Structural similarity index measurement (SSIM), Visual quality (PSNR), Normalized cross-correlation (NC).

Reversible data hiding (RDH) is a technology that embeds secret data into a carrier where both the secret data and the carrier can be recovered without any data loss. Inspired by dual images technology, this article proposes to employ a high capacity RDH scheme that is based on turtle shell (TS). We start by constructing a newly designed TS-based reference matrix. Then, two meaningful shadows will be generated after hiding the secret data in the cover image with the reference matrix’s help. Meanwhile, the location conflict problem is solved. On the decoder side, when both shadows are gathered, the data extraction and image recovery can be accomplished using the orientation relationship between two stego pixels that are located at the same coordinates in the two shadows and the reference matrix. Moreover, we introduce a security enhancement technology that improves the security of data extraction. The experiment shows that compared with other state-of-the-art RDH schemes, a higher embedding capacity is achieved by this method, and a good visual quality is retained. Simultaneously, the proposed scheme is effective against attacks on pixel value difference histograms (PDH) and regular singular (RS) analysis.

Steganography is quite possibly the best procedures to shroud the presence of concealed data inside a cowl thing. Pictures are the notable cowl things for Steganography and in this work, picture Steganography is embraced. Implanting secret information based interior pictures calls for concentrated calculation, and thus equipment based Steganography usage increases fundamental interest of Steganography based calculation. There are a few techniques to shroud inside recorded cover-photographs. The spatial area strategies control the duvet-photograph pixel-bit esteems to insert the mystery data. The mystery pieces are composed quickly to the duvet picture pixel bytes. Thusly, the spatial territory procedures are basic and simple to execute. The Least Significant Bit (LSB) based picture steganography is one of the significant systems in spatial area photograph Steganography. In this work, a shiny new strategy for LSB Steganography has been suggested that is an extemporized model of 1 bit LSB technique.

Reversible data hiding in encrypted domain (RDH-ED) technology can embed data into cover media without exposing the original content to third parties. In addition, the recipient can recover the cover media losslessly after extracting the embedded data. Image-based RDH-ED has been widely studied, but RDH-ED based on 3D meshes has obtained few research results due to the complex data structure and irregular geometric structure of 3D meshes. With the widespread application of 3D meshes, the research on 3D meshes has attracted extensive research from researchers in recent years. In this paper, we propose a reversible data hiding for encrypted 3D meshes based on integer mapping and most significant bit (MSB) prediction. The content owner divides all vertices into “embedded” sets and “reference” sets and then maps floating-point coordinates to integers. After calculating the MSB prediction error of the “embedded” sets, the encryption technology is performed. Then, additional data can be embedded through the MSB replacement strategy. According to different permissions, legal recipients can obtain the original meshes, the additional data or both of them by using the proposed separable method. Higher embedding capacity is achieved by adopting MSB embedding strategy, and perfect recovery of the original meshes is achieved by using ring prediction scheme. The experimental results show that the proposed method has greater embedding capacity compared with the state-of-the-art method.

Encryption and information hiding are widely used in security applications requiring copyright protection, over-communication, and tamper detection. Quantum steganography, using quantum images and audio as information carriers, is a fascinating hybrid of classical and quantum informatics. While existing steganography techniques offer high invisibility, current embedding capacities remain insufficient. As such, a high capacity quantum steganography algorithm, based on image interpolation, is proposed in this study. In this approach, quantum interpolation is conducted by maximizing difference values between neighboring pixels to generate a cover image. Secret message embedding and extraction steps are then developed for the resulting quantum images, in addition to corresponding quantum circuit networks. A series of simulation experiments were conducted to demonstrate the implementation and assess the performance of the proposed technique, the efficiency of which was evaluated using several performance metrics and the visual quality of the stego-images. The resulting information capacity was much higher than that of existing quantum steganography algorithms. This suggests the proposed strategy to be a promising trade-off between invisibility and capacity, providing new motivation to investigate image steganography using quantum computing resources.

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

In this digital era, an unprecedented volume of data theft and other criminal activities have rapidly increased on communication systems over wireless channels. To protect the data, various methods have been carried out over the past decade. Data hiding combined with steganography and cryptography is one of the solutions to address the data theft issue. Steganography is used to embed data inside any carrier image file, whereas cryptography is used to maintain the private information in a secure manner. In this paper, the cryptographic algorithm Rivest, Shamir & Adelman (RSA) and Elliptical Curve Cryptography (ECC) are used in cohesion with the data embedding technique. Also, this compares the performance of the encryption algorithms with the hiding technique. Here, the encrypted data is hidden inside the selected location using the Fruit fly Optimization hybridized Improved Seeker (FOIS) algorithm. After finding the optimization pixels, the difference is calculated between the optimization pixels and nearest pixels using Pixel Value Differencing (PVD) to enhance the performance. This work presents the usage of the proposed algorithm based on the evidence from the comparative analysis and threat simulation.

The aims of improving steganographic method are divided into two groups: the first is to make the hiding capacity as high as possible; the second is to make the visible distortion as low as possible. The higher the visual quality of the stego-image, the less suspicious it becomes, which can increase security. However, the distortion caused by embedding data into images is not predictable and typically image dependent. If the user has a database of possible cover images, finding a suitable cover image that can sustain high visual quality after embedding is challenging. Thus, an automatic cover selection method is needed. In this paper, the problem of visual quality of the stego-image is tackled as a classification problem, where a CNN-based classifier is employed to select images that can have high imperceptibility after the process of embedding. To achieve that, a CNN was trained to classify images into “High Quality” and “Low Quality”. The CNN was based on SqueezeNet architecture, and was trained in two scenarios; transfer learning and learning from scratch. The two classifiers were able to achieve very high classification accuracies of F1 = 0.926 and 0.904.

Reversible data hiding in encrypted images (RDHEI) is a technique that allows for secret data to be embedded in images while preserving the privacy of the image content when the image owner and the data hider are different entities. This paper proposes a new reversible data hiding scheme in encrypted images, in which the data hider does not need a data hiding key in the data embedding process. We first reserve embedding room before image encryption based on the congruence relations of pixel values within image blocks. Then, the pre-processed original image is encrypted using a multi-secret sharing based image encryption algorithm that satisfies additive homomorphism. Using the homomorphism of the image encryption, we embed the secret data into the reserved embedding room directly in the encrypted images without a data hiding key. The receiver can extract the secret data by checking the congruence relation between pixel values in a decrypted image block and then recover the original image. We analyze the security of the proposed scheme and embedding capacity with different block sizes and different parameters of transformation. The experimental results and analysis show that the proposed scheme achieves a high embedding capacity with a suitable block size especially in smooth images.

As artificially designed two-dimensional (2D) arrays of subwavelength nanostructures, metasurfaces provide a new avenuefor the design of static planar optics. However, one would prefer a dynamic modulation of the metasurface in the event of practical applications. As a potentially important technique for information security, meta-holographic encryption has the characteristics of subwavelength pixels, precise control, and high safety factor. In this paper, by integrating birefringent liquid crystals (LCs) with all-dielectric metasurfaces in combination with visual cryptography (VC), we demonstrate a tunable metasurface with an information encryption function in the visible range. In the encryption process, the secret image is hidden in a set of unidentifiable and orthogonal arranged phase-only meta-holograms with high security of concealment. In the decryption process, the generated meta-hologram is illuminated by a plane wave and an electric field across the LC layer is applied to superimpose the reconstructed holographic patterns, thus we could acquire the secret image. Therefore, the decryption method can be flexibly and sufficiently adjusted with applied field under monitorable condition. For this reason, it is useful in enhancing the diversity of information hiding methods and improving the security of information. Our method can be extended to other similar applications.

Contemporary cloud storage and social media networks have matured and; as a result, the privacy protection of image content in these applications have attracted more attention. Reversible data hiding in encrypted i mages (RDHEI) is an efficient technique to embed additional data such as timestamps, watermarks, and copyright information into encrypted images to protect the image content. This paper proposes a reversible data hiding scheme in encrypted images based on a block-based adaptive MSB e ncoding technique (BAME-RDHEI). The content owner first uses a block-based image encryption method , including block permutation , pixel permutation in each block, and the block-based bitwise exclusive-or operation, to encrypt an image. This specific image encryption method preserves the relevance of the MSB bit planes in each block, and the block-based adaptive MSB encoding technique can be applied to classify and encode eight block types according to the number of MSB bit planes where all the values are all ‘1’ or ‘0’. After embedding the indicators that are generated by Huffman coding into the first MSB bit plane of each block, the remaining values of these MSB bit planes can be vacated for data embedding. The receiver can extract additional data, decrypted the image or recover the original image according to the different keys. The experimental results demonstrate that our proposed scheme provides enhanced security, significantly improves the embedding rate, and obtains higher visual quality for the decrypted image compared to other state-of-the-art schemes.

Data hiding aims to embed a secret message into a digital object such as image by slightly modifying the object content without arousing noticeable artefacts. The resultant object containing hidden information will be sent to a desired receiver via some insecure channels, e.g. images transmitted through noisy channel, social networks are vulnerable to unknown pollution or compression by a third party, which may lead the transmitted objects to be attacked such that the reconstructed message has a significant error rate. It therefore requires us to use robust embedding strategies for data hiding to realise reliable message retrieval. To this end, in this paper, a metric model to estimate the robustness of data hiding for noisy transmission based on the statistical characteristics of cover and embedding operation is presented, the former is mainly reflected by spatial frequency and texture feature, and the latter embedding operation is mainly reflected by embedding modification. The goal is to ensure that both statistical characteristics and embedding operation can be used to maximise the embedding robustness. To the best knowledge, it is the first time to estimate robustness before data hiding by a special metric model. Experimental results show that, by combining the proposed metric model in three classical data hiding methods, i.e. BPS, DE and QIM, the robustness can be significantly improved, which demonstrates its superiority and applicability.

E-healthcare requires communication of patient report to a specialized doctor in a real time scenario. Therefore, any harm to patient medical data can lead to a faulty diagnosis that can be lethal for the patient. To ensure safe and secure communication in E-healthcare framework, an efficient reversible data hiding algorithm in encrypted domain has been proposed in this paper. The proposed algorithm gives a higher embedding rate by embedding a single bit of patient data in $$base_2$$ numeral framework at every pixel of the cover image without any occurrence of underflow and overflow problem. The proposed method has been evaluated for content authentication by subjecting it to various image processing attacks. The experimental study reveals that for test images, proposed method has higher embedding rate than the compared methods, precisely recover patient information with an average value of Peak Signal Noise Ratio value of 48.13 dB between the cover image and stego image respectively. A comparison of the observed results with that of some state-of-art methods shows that proposed method performs better and as such is an ideal candidate for content authentication of Electronic patient information in a typical E-healthcare framework.

The contemporary adoption of cloud storage and social media networks has increased the need for privacy protection of image content. A key area of research utilizes reversible data hiding in encrypted images (RDHEI) to embed additional data into an encrypted image while protecting image content from disclosure. This paper proposes an RDHEI scheme based on prediction error and block classification (PB-RDHEI). Different from the conventional schemes, we combine a prediction error matrix and the bit planes block classification technique to create a new level for an encrypted image. For each block, some MSB bit planes can be used for additional data hiding to efficiently improve upon the available reserved room. Receivers can separately extract the complete additional data or recover the original image. The experimental results show that our proposed scheme significantly improves the embedding rate and can recover the original image losslessly.