Search

Hidden capacity, concealment, security, and robustness are essential indicators of hiding algorithms. Currently, hiding algorithms tend to focus on algorithmic capacity, concealment, and security but often overlook the robustness of the algorithms. In practical applications, the container can suffer from damage caused by noise, cropping, and other attacks during transmission, resulting in challenging or even impossible complete recovery of the secret image. An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms. In this proposed algorithm, a secret image of size 256 × 256 is first decomposed using an eight-level Haar wavelet transform. The wavelet transform generates one coefficient in the approximation component and twenty-four detail bands, which are then embedded into the carrier image via a hiding network. During the recovery process, the container image is divided into four non-overlapping parts, each employed to reconstruct a low-resolution secret image. These low-resolution secret images are combined using dense modules to obtain a high-quality secret image. The experimental results showed that even under destructive attacks on the container image, the proposed algorithm is successful in recovering a high-quality secret image, indicating that the algorithm exhibits a high degree of robustness against various attacks. The proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain, making it suitable for practical applications. In conclusion, the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing algorithms. Its ability to recover high-quality secret images even in the presence of destructive attacks makes it an attractive option for various applications. Further research and experimentation can explore the algorithm's performance under different scenarios and expand its potential applications. [ABSTRACT FROM AUTHOR]

Several medical consultations and examinations have been undertaken online since the Covid outbreak. However, when private data was communicated over the internet or uploaded to the cloud, medical information became more susceptible to security risks. Steganography is a technique that can be used to hide sensitive information within a cover image. This paper presents four improved algorithms to enhance steganography's performance in medical images. A full-size hidden image that is as huge as a cover image cannot be handled by previous methods, which is what the algorithmic design is meant to address. Several creative methods are presented, including the computation of Discrete Cosine Transform (DCT) coefficients based on scaled floating values, the addition of an adaptive compression matrix, and a new approach for systematically dispersing a concealed number of bits across multiple separate locations in the cover image. The results of the experiment showed a notable advancement over the earlier research. Our secret image size is substantially larger than the past studies, yet the structure similarity index matrix (SSIM) of the best reconstructed secret image is close to ideal, the peak signal-to-noise ratio (PSNR) and the payload capacity are higher than in the previous studies. This research is beneficial since it contributes to a medical application for enhancing the security of information concealed in chest X-ray images. Medical personnel can generate an image that conceals patient information in a secure manner. [ABSTRACT FROM AUTHOR]

Steganography technology which conceals a message in a carrier to make it invisible is critical for information security. Conventional optical image steganography using diffractive optical components or spatial light modulators suffers from less encoding channel and bulky volume. The emergence of multifunctional metasurface that can manipulate abundant physical dimensions of optical fields allows the multi‐channel image steganography in a compact volume. Here, the image hiding in a metasurface is demonstrated by modulating the amplitude, phase, and polarization states of terahertz (THz) waves completely. Especially, the phase channel can decouple from the amplitude channel based on a Fresnel‐diffraction‐based algorithm. By directly measuring the phase distribution using the homemade THz focal plane imaging system, the number of transmission channels can expand from N to 2N. As a proof of concept, it is shown that the secret images can encode in the phase channel and subsequently extract by using different keys, such as polarization states, detection distances, and its combinations. Moreover, different hiding strategies with different attacker behaviors are also demonstrated. The decoupling of the phase and amplitude channels with a single metasurface may open an avenue toward innovative optoelectronic devices for optical image steganography, data storage, and terahertz communication. [ABSTRACT FROM AUTHOR]

An authenticated reversible image hiding algorithm based on blockchain framework is presented in this paper. First, global eigenvalues of the plain image are extracted as plaintext keys. Meanwhile, a novel key transformation model is built, combining with public key cryptography RSA and random numbers to compute the initial values for Lorenz system. Thereby, chaotic sequences are produced. Secondly, a preprocessed model and an integer getting model are constructed to process these chaotic sequences and generate preprocessed sequences. The digital signature image and the plain image are then encrypted to obtain a cipher signature image and a cipher image. Thirdly, integer wavelet transform is executed on the carrier image to embed the cipher signature image and the cipher image in the frequency domain matrices by the least significant bit. Next, 2 k correction method is employed to correct these coefficient matrices. Finally, inverse integer wavelet transform is performed on the coefficient matrices to generate the carrier image containing secrets. Especially, cipher signature image is stored in an interplanetary file system, producing content identifier hash value. Then content identifier hash value is written into smart contract and saved on Ethereum blockchain. In the stages of decryption and authentication, the receiver inquires the corresponding transaction file from blockchain by the transaction address, obtaining content identifier hash value and downloading cipher signature image from interplanetary file system. Consequently, the receiver extracts cipher signature image from carrier image containing secrets to do decryption and authentication. Experimental results show that the proposed image hiding algorithm has strong sensitivity to both key and plaintext, can resist clipping attack, and etc. [ABSTRACT FROM AUTHOR]

This paper presents a novel optical image hiding scheme that employs chaotic fingerprint phase masks (CFPMs) and diffractive imaging. The proposed scheme encodes the secret image as a noise-like diffractive pattern using CFPMs positioned at various diffractive distances. Principal component analysis (PCA) is employed to obtain the score matrix of the noise-like diffractive pattern, and singular values are derived from the score matrix using singular value decomposition (SVD). Additionally, the host image is subjected to a 3-level discrete wavelet transform (3-LDWT) to extract low-frequency coefficient features. The same PCA and SVD operations are performed on these features and combined with the singular values of the secret image to complete the image hiding process. This scheme effectively balances the invisibility and robustness of the hiding scheme by embedding the low-dimensional features of the secret image into the low-frequency features of the host image. Moreover, the inclusion of biometric features enhances the association with the key and authorized users, while the incorporation of chaos mapping and diffractive parameters provides additional security for the proposed scheme. The feasibility, efficiency, security, and robustness of the scheme are verified through a series of rigorous experiments. [ABSTRACT FROM AUTHOR]

The technique known as steganography involves hiding information within the information of others in order to mask the fact that communication is taking place. Nonetheless, digital photos are the most widely used carrier format because of their widespread availability online. A photo contains a lot of sensitive steganography data. While some are more complex and powerful than others, they all have their strengths and weaknesses. Depending on the application, the steganography technology used must adhere to certain specifications. For example, some applications might require that the secret data be hidden completely, while others might require that a more significant secret message be hidden. The objective of this project is to present an overview of the techniques and applications of image steganography. Additionally, it attempts to clarify what constitutes a good steganography. [ABSTRACT FROM AUTHOR]

Image encryption is an important research field. In some application scenarios, only encrypting can actually attract the attention of attackers. Therefore, this article provides dual protection for images that require confidentiality. The image is first encrypted and then hidden in the normally transmitted image to avoid attacks from attackers. The encryption adopts a combination of DNA encoding and chaos theory, while the hiding adopts LSB algorithm. The experimental results show that the algorithm has achieved good security effects, and the correlation coefficient analysis has also achieved ideal results.

Information security is one of the most important parts of information technology and communication in the modern world. Information security has gained a lot of attention recently due to the need to transmit information through global communication channels and through the Internet. In this research, the genetic algorithm is used to develop the chaotic maps that are randomly generated by the chaotic system to find the best map by which the locations of image data are inferred to be hidden in the audio. An average image size of 64×64 pixels is used to be hidden in an audio recording of 7 seconds duration at average resolution specifications. The results show that one of the chaotic maps created at the 739 generations is the best in determining image byte hiding locations in the high-frequency band of the audio. The quality measures show the cover audio that includes the hidden image is not much different from the original input one. This confirms the validity of the proposed method and the good selection of hiding locations in accordance with information security concepts. [ABSTRACT FROM AUTHOR]

Recently, many researchers have proposed deep neural network (DNN) watermarking technologies, DNN watermarking approaches can be divided into two categories: static watermarking and dynamic watermarking methods. A static watermark is embedded into the internal parameters of a DNN model, but a dynamic watermark relies on the specific training data of the DNN model and uses the associated neuron activation map or the output result by the DNN model to extract the watermark information. Dynamic watermarks mostly use DNN application programming interfaces(APIs) to remotely access DNN models and extract their watermarks to prove their copyright, so dynamic watermarking technology is more popular. According to the distribution inconsistency between a dynamic watermark and training data, an attacker can detect the dynamic watermark, so that the model owner cannot obtain the desired prediction results and then verify the copyright of the suspect model. To this end, we propose a dynamic watermarking approach based on a reversible image hiding network, which improved the undetectability of a DNN watermark, and it can perfectly reconstruct the secret image as the copyright logo of a DNN model. We perform our work on the MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100, and Caltech-101 datasets. The experimental results show that our method has higher DNN watermarking accuracy and higher undetectability with no significant side effects on the main functions of the host DNN model. [ABSTRACT FROM AUTHOR]

Image hiding secures information security in multimedia communication. Existing deep image hiding methods usually process the secret and cover information at first, and then fuse such entire processed information. This complete and rough fusion pipeline, however, severely hinders the quality improvement of the stego and revealed secret images. This paper proposes a deep image hiding architecture, named Deep Adaptive Hiding Network (DAH-Net), to gradually extract and fuse the necessary secret and cover information at the frequency and the depth (layer) extents. Specifically, we propose the Attentive Frequency Extraction method for the DAH-Net to adaptively extract the necessary secret and cover information at the frequency level. The Gradual Depth Extraction method is further proposed for the DAH-Net to gradually extract and fuse the attentive frequency secret and cover information at the depth (layer) level of the deep image hiding network. Extensive experiment results demonstrate the proposed DAH-Net is more universal and achieves state-of-the-art performances in image hiding, watermarking, and photographic steganography. [ABSTRACT FROM AUTHOR]

An image hiding scheme based on stochastic moiré gratings is proposed, discussed, and illustrated in this paper. The proposed scheme is based on a counter-intuitive optical feature of specially designed stochastic moiré gratings when similar images in the static mode become very different in the time-averaged mode. A soft computing PSO algorithm was used for the construction of stochastic gratings. Complex computational algorithms were required to construct the cover image; however, the decryption process was completely visual. The cover image must oscillate in a predefined direction and at a predefined amplitude (the amplitude of the harmonic oscillation is one of the parameters of the proposed image hiding scheme). Computational experiments were used to demonstrate the efficacy of this optical image hiding scheme based on the stochastic moiré gratings. [ABSTRACT FROM AUTHOR]

Image steganography is a procedure of hiding any messages within an image. In this paper, our major goal is to conceal images within audio, and we converted this audio steganography problem to image steganography by utilizing the mel-spectrogram of the audio files as the cover medium. Previously, this audio steganography problem was implemented using statistical methods like least significant bit (LSB) encoding. Here we explore the use of deep neural networks (DNNs), and we propose a new technique to hide images within the audio using deep generative models which allow us to optimize the perceptual quality of the reconstructed audio and image by our model. We showed that our model efficiently hides images within audio and evades detection by steganalysis tools, is robust to different color spectrum images, and can hide multiple image data in single audio. [ABSTRACT FROM AUTHOR]

Image coding technology has become an indispensable technology in the field of modern information. With the vigorous development of the big data era, information security has received more attention. Image steganography is an important method of image encoding and hiding, and how to protect information security with this technology is worth studying. Using a basis of mathematical modeling, this paper makes innovations not only in improving the theoretical system of kernel function but also in constructing a random matrix to establish an information-hiding scheme. By using the random matrix as the reference matrix for secret-information steganography, due to the characteristics of the random matrix, the secret information set to be retrieved is very small, reducing the modification range of the steganography image and improving the steganography image quality and efficiency. This scheme can maintain the steganography image quality with a PSNR of 49.95 dB and steganography of 1.5 bits per pixel and can ensure that the steganography efficiency is improved by reducing the steganography set. In order to adapt to different steganography requirements and improve the steganography ability of the steganography schemes, this paper also proposes an adaptive large-capacity information-hiding scheme based on the random matrix. In this scheme, a method of expanding the random matrix is proposed, which can generate a corresponding random matrix according to different steganography capacity requirements to achieve the corresponding secret-information steganography. Two schemes are demonstrated through simulation experiments as well as an analysis of the steganography efficiency, steganography image quality, and steganography capacity and security. The experimental results show that the latter two schemes are better than the first two in terms of steganography capacity and steganography image quality. [ABSTRACT FROM AUTHOR]

Currently, steganography is considered the most powerful technique among other procedures to obscure the presence of buried information within a cover body. The Greek word steganographic means covered writing. This term may be considered the most appropriate to bury confidential secret data via internet correspondence. Steganography technique can be defined as the science of safe electronics transmitting. So, the aims of the steganography technique are to hide the presence of conveying data from un unwilling party. Various carrier object formats can be implemented, but color images become the more common among the other digital object. Currently, a lot of works adopted the image steganography as a cover object. The contemporary secure image steganography technique offerings a transferring the embedded confidential information task to the intended end point without being perceived or even suspicious by the attackers. There exists a large variability of steganographic techniques for fixed secret data in color images. Some of these techniques are more complicated than others, and each of these methods has positive and negative considered points. The objective of this work is to give a brief overview of steganography techniques using color images as a cover object by implementing MATLAB software using the LSB algorithm. We found that the carrier image keeps the original feature without any distortion caused by embedded secret information. The performance measured by PSNR and MSE technique and shown satisfied results. [ABSTRACT FROM AUTHOR]

Convert Image Communication (CIC) is a promising technology to protect the privacy of images. Recently, the emergence of robust CIC resistant to JPEG compression has gained due to the widespread use of JPEG compression in image communication. This paper introduces a R ̲ obust image hiding network with F ̲ requency and S ̲ patial A ̲ ttentions (RFSA) to implement robust CIC. RFSA can hide an image within another image with high robust. It incorporates multiple image attentions corresponding to imperceptibility, recovered image quality, and resistance to JPEG compression, which ensure that secret images are hidden within regions that cause little distortion and can well withstand JPEG compression. Additionally, two encoders, that is, a frequency encoder and a spatial encoder, are mixed to adaptively embed secret images across both frequency and spatial domains. Experimental results demonstrate that the proposed scheme not only maintains high image quality and capacity but also exhibits exceptional resistance to JPEG compression compared to other state-of-the-art image hiding methods. The average Peak Signal-to-Noise Ratio (PSNR) of the recovered image remains at 24.96 dB even under JPEG compression with a quality factor of 55. • We use frequency domain to capture the intrinsic DCT changing in JPEG compression. • Multi-attentions are derived based on compression affect and the human visual system. • Experiments show high image quality and resilience to JPEG compression of our scheme. [ABSTRACT FROM AUTHOR]

At present, there is always a potential threat in the process of information transmission. As a way to protect data security, image hiding has attracted extensive attention. Current image hiding algorithms have insufficient resistance to deep leaning based steganalysis algorithms and relatively low hiding capacity. This paper presents an image hiding algorithm based on a generative feedback residual network (GFR-Net), which hides multiple color secret images in a single color carrier image. First, several secret images and a carrier image were fed into the image hiding network, in which the secret images were embedded into the carrier image, resulting in an output of container image. A recovery network also based on GFR-Net was designed to reconstruct the secret images from the container. The extensive experiments for hiding normal and encrypted images show that the proposed image hiding model has a good performance in terms of payload and security. [ABSTRACT FROM AUTHOR]

An image hiding scheme based on stochastic moiré gratings is proposed, discussed, and illustrated in this paper. The proposed scheme is based on a counter-intuitive optical feature of specially designed stochastic moiré gratings when similar images in the static mode become very different in the time-averaged mode. A soft computing PSO algorithm was used for the construction of stochastic gratings. Complex computational algorithms were required to construct the cover image; however, the decryption process was completely visual. The cover image must oscillate in a predefined direction and at a predefined amplitude (the amplitude of the harmonic oscillation is one of the parameters of the proposed image hiding scheme). Computational experiments were used to demonstrate the efficacy of this optical image hiding scheme based on the stochastic moiré gratings.

A computational technique for the determination of optimal hiding conditions of a digital image in a self-organizing pattern is presented in this paper. Three statistical features of the developing pattern (the Wada index based on the weighted and truncated Shannon entropy, the mean of the brightness of the pattern, and the p-value of the Kolmogorov-Smirnov criterion for the normality testing of the distribution function) are used for that purpose. The transition from the small-scale chaos of the initial conditions to the large-scale chaos of the developed pattern is observed during the evolution of the self-organizing system. Computational experiments are performed with the stripe-type patterns, spot-type patterns, and unstable patterns. It appears that optimal image hiding conditions are secured when the Wada index stabilizes after the initial decline, the mean of the brightness of the pattern remains stable before dropping down significantly below the average, and the p-value indicates that the distribution becomes Gaussian. [ABSTRACT FROM AUTHOR]

Image coding technology has become an indispensable technology in the field of modern information. With the vigorous development of the big data era, information security has received more attention. Image steganography is an important method of image encoding and hiding, and how to protect information security with this technology is worth studying. Using a basis of mathematical modeling, this paper makes innovations not only in improving the theoretical system of kernel function but also in constructing a random matrix to establish an information-hiding scheme. By using the random matrix as the reference matrix for secret-information steganography, due to the characteristics of the random matrix, the secret information set to be retrieved is very small, reducing the modification range of the steganography image and improving the steganography image quality and efficiency. This scheme can maintain the steganography image quality with a PSNR of 49.95 dB and steganography of 1.5 bits per pixel and can ensure that the steganography efficiency is improved by reducing the steganography set. In order to adapt to different steganography requirements and improve the steganography ability of the steganography schemes, this paper also proposes an adaptive large-capacity information-hiding scheme based on the random matrix. In this scheme, a method of expanding the random matrix is proposed, which can generate a corresponding random matrix according to different steganography capacity requirements to achieve the corresponding secret-information steganography. Two schemes are demonstrated through simulation experiments as well as an analysis of the steganography efficiency, steganography image quality, and steganography capacity and security. The experimental results show that the latter two schemes are better than the first two in terms of steganography capacity and steganography image quality.

Steganography techniques leads main role in embedding process of secret information into cover information in order to provide good security. These techniques are very essential, when sending the confidential information from one place to another place in the field of defense and medical. Hence, it is a challenging and important task to hide the secret information in to cover information. This review provides classification of steganography techniques, existing and proposed techniques with advantages and disadvantages. As per observations of existing methods, much amount of information hides using spatial domain techniques than transform domain techniques. This review mainly focuses on the spatial domain image steganography techniques. [ABSTRACT FROM AUTHOR]

A computational technique for the determination of optimal hiding conditions of a digital image in a self-organizing pattern is presented in this paper. Three statistical features of the developing pattern (the Wada index based on the weighted and truncated Shannon entropy, the mean of the brightness of the pattern, and the p-value of the Kolmogorov-Smirnov criterion for the normality testing of the distribution function) are used for that purpose. The transition from the small-scale chaos of the initial conditions to the large-scale chaos of the developed pattern is observed during the evolution of the self-organizing system. Computational experiments are performed with the stripe-type patterns, spot-type patterns, and unstable patterns. It appears that optimal image hiding conditions are secured when the Wada index stabilizes after the initial decline, the mean of the brightness of the pattern remains stable before dropping down significantly below the average, and the p-value indicates that the distribution becomes Gaussian.

Data security is one of the most important sciences nowadays. There is a huge amount of data transferred over the internet each moment and this data should be secured. Steganography is a type data security techniques that is used to hide the secret message into a cover object. Image steganography is the technique that hides an image in another image. This paper proposed a technique that depends on Lower-Upper (LU) decomposition. In the proposed technique LU decomposition is applied for both cover and secret images. The proposed method was tested using some gray and color images. The proposed technique achieved high results with reference to Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Normalized Cross-Correlation (NCC). The PSNR for the cover image is ranging from 36 to 44 dB. The similarity between the secret image and the extracted image is 100% and the NCC is 1. [ABSTRACT FROM AUTHOR]

Data hiding is the technique of embedding data into video or audio media. With the development of deep neural networks (DNN), the quality of images generated by novel data hiding methods based on DNN is getting better. However, there is still room for the similarity between the original images and the images generated by the DNN models which were trained based on the existing hiding frameworks to improve, and it is hard for the receiver to distinguish whether the container image is from the real sender. We propose a framework by introducing a key_img for using the over-fitting characteristic of DNN and combined with difference image grafting symmetrically, named difference image grafting deep hiding (DIGDH). The key_img can be used to identify whether the container image is from the real sender easily. The experimental results show that without changing the structures of networks, the models trained based on the proposed framework can generate images with higher similarity to original cover and secret images. According to the analysis results of the steganalysis tool named StegExpose, the container images generated by the hiding model trained based on the proposed framework is closer to the random distribution.

Abstract In this paper, a novel method of image extraction is proposed. Firstly, the image information is embedded into the parameters of the chaotic system, and then the image is overlapped and embedded to complete the image hiding. This process is equivalent to a dynamic system with unknown time-varying parameters. Secondly, the D-type iterative learning control algorithm is used to extract the information hidden in the image, because iterative learning can be used to estimate the time-varying parameter system completely in the time interval. Finally, the numerical simulation shows that the algorithm can effectively extract the hidden information under various attacks.

Recently, Internet becomes a most common medium for transferring critical data and the security of the transmitted data gains maximum priority. Image steganography has been developed as a well‐known model of data hiding which verifies the security level of the transferred data. The images offer high capacity, and the occurrence of accessibility over the Internet is more. An effective steganography model is required for achieving better embedding capacity and also maintaining the other variables in an acceptable value. This article introduces a new robust image steganography using Teaching Learning Based Optimization (TLBO) edge detection model. The TBLO is basically a metaheuristic algorithm which is inspired from the teaching and learning procedure in classrooms. The former stage indicates the learning from the teacher and the latter phase represents the interaction among the learners. The experimental validation takes place in a comprehensive way under several views and the outcome pointed out the superior results of the presented model. [ABSTRACT FROM AUTHOR]

Image hiding is a field of research that focuses on covert storage and transmission techniques. It involves embedding a secret image within a container image to create a classified-carrying image that resembles a normal image. Nevertheless,the current image hiding methods based on Invertible neural networks suffer from a critical issue of information loss during the hiding process. This leads to a substantial degradation in the quality of the secret image extracted,thereby preventing the simultaneous achievement of secure transmission, high-capacity transmission, and high fidelity of the secret image in an insecure network environment. To address this issue,we introduce a novel image hiding architecture called FMIN (Fitting Models Based on Invertible Network). FMIN incorporates our innovative fitting model, which fits the loss information and generates a variable z simulating the loss information at the receiver side. This variable z is used as an input to the revealing process,enabling the high-quality extraction of multiple secret images from a single classified loaded image. Additionally,we introduce a novel decoupled training strategy aimed at enhancing the stability of image hiding model during training. Experimental results demonstrate that the image hiding method based on the proposed FMIN architecture in this paper significantly outperforms other SOTA image hiding methods for single-image and multiple-images hiding. [ABSTRACT FROM AUTHOR]

Image hiding is the process of hiding secret image into cover image, and revealing secret image from steganographic image. However, the quality of generated images and the ability to resist steganalysis detection of steganographic image can be further improved. We propose a novel double-layer U-Net image hiding method based on improved Inception and attention mechanism. The secret image undergoes Haar wavelet transform, hiding network hides the wavelet sub-band of secret image into cover image. We use improved Inception while cascading convolution and attention mechanisms to better extract and fuse information, resulting in high-quality steganographic image. In the revealing stage, Attention mechanism can help networks extract more features about secret image, then we using inverse Haar wavelet transform to revealing high-quality secret image. The experimental results show that the method can obtain high quality images and have good security. When hiding multiple images, the method still has good performance. • We propose a novel double-layer U-Net structure that can fully extract and fuse image features, generate high-quality image, and still have good performance when hiding multiple images. • Our method improves the anti-steganalysis ability of steganographic image. By cascading convolution and attention mechanism, secret image can be hidden in more suitable locations when hiding image, improving the security of steganographic image. • Our method has strong generalization ability. The improved Inception module expands the network width, extract features from multiple scales, thereby fusing feature information from different dimensions. [ABSTRACT FROM AUTHOR]

The objective of image hiding is to conceal a secret image in a cover image inconspicuously. The challenges for image hiding focus on the embedding capacity, image quality and security. To contend with these challenges, this paper presents a novel image hiding method using Invertible Rescaling Net (IRN) and Similarity of Bits Pairs (SBP). Because of the downscaling ability of IRN, our method is able to hide a secret image into a cover image with the same dimensions. Moreover, the superior rescaling capabilities of IRN enable us to produce a recovered image of high-quality. We improve the SBP embedding technique and combine it with differential coding to boost the quality of the stego image. In the experiments, the quality of the recovered secret image and stego image is assessed using various quality evaluation metrics, and for the security, the anti-steganalysis performance is also evaluated in this paper. Extensive experimental results reveal that the quality of both the recovered secret image and the stego-image in our method outperforms several advanced methods and provides a high degree of security. • We apply invertible rescaling net (IRN) to image hiding task for the first time. • We improve the SBP encoding algorithm to prevent the degradation of the quality of the stego image. • Our method considers the robustness against the classical adversary's steganalysis. [ABSTRACT FROM AUTHOR]

Highlights • First comprehensive literature review on optical image hiding and watermarking. • Past existing works are only on optical encryption or digital watermarking. • Various optical systems and architectures for image hiding are reviewed. • Processing algorithms related to optical image hiding are summarized. • Comparison of different optical image hiding schemes is made. Abstract Information security is a critical issue in modern society and image watermarking can effectively prevent unauthorized information access. Optical image watermarking techniques generally have advantages of parallel high-speed processing and multi-dimensional capabilities compared to digital approaches. This paper provides a comprehensive review of the research works related to optical image hiding and watermarking techniques conducted in the past decade. The past research works have focused on two major aspects: various optical systems for image hiding, and the methods for embedding the optical system output into a host image. A summary of the state-of-the-art works is made from these two perspectives. [ABSTRACT FROM AUTHOR]

Image steganography aims to achieve covert communication between two partners utilizing stego images generated by hiding secret images within cover images. Existing deep image steganography methods have been rapidly developed in this area. Such methods, however, usually generate the stego images and reveal the secret images using one-process networks, lacking sufficient refinement in these methods. Thus, the security and quality of stego and revealed secret images still have much room for promotion, especially for large-capacity image steganography. This paper proposes Joint Adjustment Image Steganography Networks (JAIS-Nets), containing a series of coarse-to-fine iterative adjustment processes, for image steganography. Our JAIS-Nets first proposes Cross-Process Contrastive Refinement (CPCR) adjustment method, using the cross-process contrastive information from cover-stego and secret-revealed secret image pairs, to iteratively refine the generated stego and revealed secret images, respectively. In addition, our JAIS-Nets further proposes Cross-Process Multi-Scale (CPMS) adjustment method, using the cross-process multi-scale information from different scales cover-stego and secret-revealed secret image pairs, to directly adjust and enhance the intermediate representations of the proposed JAIS-Nets. Integrating the proposed CPCR with CPMS methods, the proposed JAIS-Nets can jointly adjust the quality of the stego and revealed secret images at both the learning process and image scale levels. Extensive experiments demonstrate that our JAIS-Nets can achieve state-of-the-art performances on the security and quality of the stego and revealed secret images on both the regular and large capacity image steganography. [ABSTRACT FROM AUTHOR]