Large Embedding Capacity Research Articles

Improved Face Image Authentication Scheme based on Embedding in Adjacent Coefficients

Face image authentication (FIA) schemes have recently been developed using face detection and image watermarking technology. The research in this direction proved the presented schemes' efficiency in accurately detecting the manipulated face regions and recovering the original face region. Recovering the original face region is very important in practical applications. Still, it was at the cost of increasing the secret data that must be embedded in the face image. The increment in the secret data required a large embedding capacity, which was not available in some images. To overcome this limitation, an improved FIA scheme based on a new data embedding algorithm is presented in this paper. The suggested FIA scheme consists of two main algorithms applied at the sender and receiver sides, where both start by detecting the face region and dividing and classifying the image into blocks that belong to the face region or outside the face region. At the sender side, the secret data are generated from the face region and embedded in the blocks outside the face region using the suggested algorithm called Embedding in Adjacent Coefficients (EAC) for three subbands obtained after applying the Slantlet transform of the blocks. On the receiver side, the secret data are extracted from the blocks outside the face region using the suggested algorithm called Extraction from Adjacent Coefficients (ExAC). The extracted data is used to authenticate the face region and recover the original one when manipulations occur. The proposed FIA scheme obtained higher embedding capacity than previous ones, making it applicable to protect more face images that could not be protected using previous FIA schemes.

A secure image steganography based on LSB technique and 2D chaotic maps

This paper introduces a pioneering, effective data-hiding algorithm using different sizes of cover images and different types of secret data (image, txt, and audio). The proposed algorithm is based on a hybrid 1D chaotic map combined with a 2D composite chaotic map paired with optimization and Least Significant Bits (LSB) techniques. We employ chaotic maps to create the initial embedding position vector, and LSB to hide the secret message in the cover image. The optimization technique utilizes the chaotic maps to optimize the random embedding sites, looking for value matches between the cover image pixels and the secret data bits. This partial improvement leads to a high-quality stego-image, a large embedding capacity, imperceptibility to the human eye, undetectability, and enhanced security. The outcomes of the extensive experiments show that when the payload is 4.2 bits per pixel, the peak signal-to-noise ratio value (PSNR), which expresses the visual quality, is above 30 dB. Furthermore, the experimental results illustrate that the system exhibits robustness against various types of attacks. As a final point of strength, the large key space (2^206) prevents the secret data from being resolved if stego-images are accessed by intruders or third parties. Even minor changes in key bits can significantly alter the extracted secret data, resulting in entirely different secret data.

Bi-directional Block Encoding for Reversible Data Hiding over Encrypted Images

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

Robust Image Steganography: Hiding Messages in Frequency Coefficients

Steganography is a technique that hides secret messages into a public multimedia object without raising suspicion from third parties. However, most existing works cannot provide good robustness against lossy JPEG compression while maintaining a relatively large embedding capacity. This paper presents an end-to-end robust steganography system based on the invertible neural network (INN). Instead of hiding in the spatial domain, our method directly hides secret messages into the discrete cosine transform (DCT) coefficients of the cover image, which significantly improves the robustness and anti-steganalysis security. A mutual information loss is first proposed to constrain the flow of information in INN. Besides, a two-way fusion module (TWFM) is implemented, utilizing spatial and DCT domain features as auxiliary information to facilitate message extraction. These two designs aid in recovering secret messages from the DCT coefficients losslessly. Experimental results demonstrate that our method yields significantly lower error rates than other existing hiding methods. For example, our method achieves reliable extraction with 0 error rate for 1 bit per pixel (bpp) embedding payload; and under the JPEG compression with quality factor QF=10, the error rate of our method is about 22% lower than the state-of-the-art robust image hiding methods, which demonstrates remarkable robustness against JPEG compression.

Reversible Data Hiding in Encrypted Images Based on Hybrid Prediction and Huffman Coding

As an interesting technique that allows data extraction and image recovery without any loss, reversible data hiding in encrypted images is an area of great concern in the field of information security. In this paper, a new reversible data hiding method with high embedding capacity is proposed based on hybrid prediction and Huffman coding. The combination of two embedding mechanisms is innovatively designed to improve the embedding capacity according to different parts of the original image, i.e., the most significant bit-plane and the remaining seven bit-planes. In the first part of this method, the prediction value of each pixel is obtained by calculating the average value of its two neighboring pixels, and all of the most significant bits can be vacated to accommodate additional data. In the second part, the prediction value of each pixel is calculated using the median edge detector predictor, on which the tag map is built. Then, Huffman coding is used to compress the tag map so that a large amount of vacant space is obtained. Finally, the secret data can be embedded into the vacated space by directly using bit substitution. Compared with some recently reported methods, experimental results and analysis have demonstrated that an original image with high visual symmetry/quality can be recovered. Also, larger embedding capacity can be achieved, such as 3.3894 bpp and 3.2824 bpp, for BOSSBase and BOWS2 databases, respectively.

A Universal Reversible Data Hiding Method in Encrypted Image Based on MSB Prediction and Error Embedding

Image encryption is used for privacy protection in cloud computing. Nowadays, reversible data hiding in encrypted image (RDHEI) has achieved great success with the demand of embedding additional information into encrypted image. The existing algorithms cannot implement large embedding capacity and good reconstructed image quality simultaneously. Besides, the universality of some methods is limited when they are used in images with different textural characteristics. In this work, an RDHEI method based on most significant bit (MSB) prediction and error embedding is proposed. On one hand, all types of prediction errors are considered in the proposed method, therefore all the pixels with prediction errors can be recovered correctly. On the other hand, error blocks are utilized to mark the locations of prediction errors and message blocks are utilized to embed data. Moreover, flag blocks are utilized to distinguish error and message blocks. To solve the problem of misjudgement for flag blocks in the decoding phase, special operations are conducted on error blocks and message blocks. Experimental results demonstrate that, compared with the state-of-the-art RDHEI methods, the proposed method has good universality on well-known databases.

Dual-image reversible data hiding method using maximum embedding ability of each pixel

Compared to conventional reversible data hiding (RDH) methods, dual-image RDH methods have greater embedding capacity, better stego image quality and higher security. Among existing dual-image RDH methods, three methods of Lu et al., Yao et al. and Kim et al. are remarkable. These three methods have much higher embedding capacities than other methods, while still maintaining stego image quality. However, these three methods all have a limitation that in order to avoid underflow/overflow, they have to ignore pixels around two pixels with values 0 and 255. In many cases, the number of such pixels is not small and this limits the embedding capacity of all three methods above. In this paper, the maximum number of bits that can be embedded in each pixel is evaluated correctly. Embedding a number of bits less than or equal to the maximum number at each pixel will not cause the underflow/overflow. Therefore the proposed method can embed data bits in all pixels of an original image. As a result, the proposed method has not only a larger embedding capacity, but also better stego image quality than mentioned above methods. With an embedding ratio of 3.45 bits per a pixel, the proposed method has a greater embedding capacity than existing dual image reversible data hiding methods while maintaining good image quality.

An End-to-End Robust Video Steganography Model Based on a Multi-Scale Neural Network

The purpose of video steganography is to hide messages in the video file and prevent them from being detected, and finally the secret message can be extracted completely at the receiver. In this paper, an end-to-end video steganography based on GAN and multi-scale deep learning network is proposed, which consists of the encoder, decoder and discriminator. However, in the transmission process, videos will inevitably be encoded. Thus, a noise layer is introduced between the encoder and the decoder, which makes the model able to resist popular video compressions. Experimental results show that the proposed end-to-end steganography has achieved high visual quality, large embedding capacity, and strong robustness. Moreover, the proposed method performances better compared to the latest end-to-end video steganography.

A Review of Motion Vector-Based Video Steganography

Steganography is a popular research direction in the field of information security. Due to the widespread use of video media, video steganography has received much attention from the research community. Among video steganography, motion vector (MV)-based video steganography has become one of the critical concerns of researchers due to its large embedding capacity and high visual quality. In this article, we focus on the research of MV-based video steganography. Firstly, the basic principles and evaluation criteria for MV-based steganography are discussed. Secondly, according to the different technical characteristics, the MV-based steganography is divided into three categories: the traditional MV domain steganography, the code-based MV domain steganography, and the adaptive MV domain steganography based on the framework of minimizing embedding distortion. The advantages and possible improvement directions of the above representative methods are illustrated. And then, the MV-based video steganalysis is outlined according to different perspectives of feature extraction, which is conducive to the design of better steganography algorithms. Finally, five future research directions are presented, such as designing distortion functions based on multiple factors, embedding methods based on new video coding standards, deep learning-based MV steganography, multidomain embedding strategies, and moving the MV-based steganography from the laboratory into the real world.

Anti-Forensics with Steganographic File Embedding in Digital Image Using Genetic Algorithm

In this study, a steganography method on digital images as anti-forensics by utilizing genetic algorithms was proposed. Genetic Algorithms are artificial intelligence whose functions are optimization and search. The purpose of this research is to optimize steganography as anti-forensic by applying a Genetic Algorithm and combined with the Hilbert curve, lempel Ziv Markov chain, and least significant bit. The result provides a new steganography method by combining various existing methods. The proposed method will be tested for image quality using PSNR, SSIM, Chi-Squared steganalysis and RS-Analysis, and extraction test. The novelty obtained from the developed method is that the steganography method is as optimal as anti-forensic in keeping confidential data, has a large embedding capacity, and is able to be undetected using forensic methods. The results can maintain data confidentiality, have a large embedding capacity, and are able to be undetected using forensic methods. The proposed method got better performance rather than the previous method because PSNR and SSIM values are high, secret data can be received back as long as the pixel value doesn't change, and the size of the embedding capacity. The proposed method has more ability to embed various types of payload/ secret data because of the way it works, which splits byte files into binary. The proposed method also has the ability not to be detected when forensic image testing is carried out.

A Data Hiding Method Based on Partition Variable Block Size with Exclusive-or Operation on Binary Image

In this paper, we propose a high capacity data hiding method applying in binary images. Since a binary image has only two colors, black or white, it is hard to hide data imperceptible. The capacities and imperception are always in a trade-off problem. Before embedding we shuffle the secret data by a pseudo-random number generator to keep more secure. We divide the host image into several non-overlapping (2n+1) by (2n+1) sub-blocks in an M by N host image as many as possible, where n can equal 1, 2, 3 , …, or min(M,N). Then we partition each sub-block into four overlapping (n+1) by (n+1) sub-blocks. We skip the all blacks or all whites in each (2n+1) by (2n+1) sub-blocks. We consider all four (n+1) by (n+1) sub-blocks to check the XOR between the non overlapping parts and center pixel of the (2n+1) by (2n+1) sub-block, it embed n 2 bits in each (n+1) by (n+1) sub-block, totally are 4*n 2 . The entire host image can be embedded 4×n 2×M/(2n+1)×N/(2n+1) bits. The extraction way is simply to test the XOR between center pixel with their non-overlapping part of each sub-block. All embedding bits are collected and shuffled back to the original order. The adaptive means the partitioning sub-block may affect the capacities and imperception that we want to select. The experimental results show that the method provides the large embedding capacity and keeps imperceptible and reveal the host image lossless.

Image Steganography Based Spatial and Transform Domain Techniques: A Review

The amount of data shared online today is increasing. Data security is therefore cited as a significant problem while processing data exchanges through the Internet. Everyone needs the security of their data during communication processes. The science and art of steganography is the concealment of one audio, message, video, or image by embedding another audio, message, video, or image in its place. It is employed to protect sensitive data against malicious assaults. In order to detect the numerous methods employed with digital steganography, this study seeks to identify the primary image-based mediums. As a result, in the spatial domain of the digital medium, the LSB approach was mostly employed, whereas in the transform domain, DTC and DWT were separated as the primary techniques. Due to its simplicity and large embedding capacity, the spatial domain was the most frequently used domain in digital steganography.

Generating visually secure encrypted images by partial block pairing-substitution and semi-tensor product compressed sensing

Recently visually secure image encryption scheme attracts more and more researchers' attention on the grounds that it will not stimulate the attackers' curiosity. Generally, four aspects should be paid great attention in visually meaningful encryption schemes, including the similarity between the cipher image and carrier image, the large embedding capacity which can embed the secret image, the satisfactory recovered quality and the robustness to resist various attacks. To improve the visual quality of the meaningful cipher image and the embedding capacity, this paper proposes a novel visually secure image encryption scheme by combining semi-tensor product compressed sensing and partial block pairing-substitution technique. The blocks of two images which have similar Standard Deviation values are selected to execute the block pairing-substitution. This makes the cipher image more similar to the carrier image, and the secret is more difficult to detect. The metric MSSIM values are all exceeding 0.91, and a close value for two-dimensional detrended fluctuation (2D-DFA), which verifies the high similarity between the cipher image and the carrier image to a greater extent. The embedding capacity can reach four times of the plain image in theory, when the compression ratio is 0.25, which demonstrates the large embedding capacity of the proposed scheme. Furthermore, less computational and spatial resources are required since the incorporation of semi-tensor product compressed sensing and cellular automata. Importantly, the embedding capacity increases as the embedding is performed on the spatial domain. Both the simulation results and theoretical analysis demonstrate the high quality of the cipher images and the high security of the proposed scheme.

High-capacity separable reversible data-Hiding method in encrypted images based on block-level encryption and Huffman compression coding

Reversible data hiding in encrypted images (RDHEI) is a technology that embeds data directly in the encrypted images without decryption or knowledge of the content of the images. However, it is difficult to vacate room from the encrypted image. This problem limits the embedding capacity of the existing RDHEI methods. In this paper, we propose a new RDHEI method designed to supply large embedding capacity without pre-processing of the original images. Using a block-level encryption scheme, the proposed method partially retains the spatial correlation in the high bit-planes of the encrypted image, while the content of the image is held securely. The data hider can compress the high bit-planes of the encrypted image to vacate high-capacity room using Huffman compression coding. In addition, image recovery and data extraction are separable and error-free at the receiver side. The receiver can retrieve the lossless original image without the embedded data using only an encryption key or by extracting the secret data directly from the encrypted image without decryption using only the data-hiding key. The experimental results and comparison proved that the proposed method performs better than previous methods in terms of embedding capacity and visual quality.

Reversible data hiding for encrypted image based on adaptive prediction error coding

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.

An Adaptive Reversible Data Hiding Scheme Using AMBTC and Quantization Level Difference

Hiding a message in compression codes can reduce transmission costs and simultaneously make the transmission more secure. This paper presents an adaptive reversible data hiding scheme that is able to provide large embedding capacity while improving the quantity of modified images. The proposed scheme employs the quantization level difference (QLD) and interpolation technique to adaptively embed the secret information into pixels of each absolute moment block truncation coding (AMBTC)-compressed block, except for the positions of two replaced quantization levels. The values of QLD tend to be much larger in complex areas than in smooth areas. In other words, our proposed method can obtain good performance for embedding capacity and still meets the requirement for better modified image quality when the image is complex. The performance of the proposed approach was compared to previous image hiding methods. The experimental results show that our approach outperforms referenced approaches.

Reversible data hiding in adjacent zeros

Data hiding techniques are used to embed information into digital objects for the purpose of copyright protection, covert communication, media notation, among other applications. Compared with traditional data hiding techniques, reversible data hiding (RDH) can restore the original digital object after extracting the hidden data. This makes RDH very useful for applications requiring high fidelity of multimedia content, such as medical records, remote sensing, and military aerial intelligence gathering. In this paper, we describe an efficient reversible data hiding algorithm that can be applied to standard and medical images, with better results expected for medical images. The algorithm is simple, blind, and offers a relatively large embedding capacity while keeping low visual distortions. In addition, it can be extended to multiple layers embedding with an acceptable visual quality. The main idea on which the proposed algorithm is based is to scan the image for pixels that have adjacent zeros at predefined positions in their binary representation. These pixels will be marked and chosen to embed the bits of a given watermark in the positions of the predefined adjacent zeros found in the selected pixels. The proposed algorithm has been successfully applied to a wide range of medical and standard images. Experimental results demonstrate the ability of the proposed algorithm to achieve high embedding capacity and high image quality.

A Video Steganography Method Based on Transform Block Decision for H.265/HEVC

High definition video application has drawn a lot of interest both from academy and industry. The relevant latest video coding technology, H.265/HEVC has been a promising area for video steganography. In this paper, we present a novel and efficient video steganography method based on transform block decision for H.265. In order to improve the visual quality of carrier video, we analyze the embedding error of data hiding with modifying partitioning parameters of CB, PB and TB, and modify the transform block decision to embed secret message and update corresponding residuals synchronously. In order to limit embedding error, we utilize an efficient embedding mapping rule which can embed N (N>1) bits message and at most modify one bit transform partitioning flag. Our experimental results show that the proposed method can achieve better visual quality, larger embedding capacity and less bit-rate increase than state-of-the-art researches.

High capacity reversible data hiding in encrypted images using SIBRW and GCC

In this paper, a reversible data hiding in encrypted images (RDHEI) method combining GCC (group classification encoding) and SIBRW containing sixteen image-based rearrangement ways is proposed to achieve high-capacity data embedding in encrypted images. Each way of SIBRW aims at bringing strongly-correlated bits of each higher bit-plane together by rearranging each higher bit-plane. For each higher bit-plane, the optimal way achieving the most concentrated aggregation performance is selected from SIBRW to rearrange this bit-plane, and then, GCC compresses the rearranged bit-plane in group-by-group manner. By making full use of strong-correlation between adjacent groups, GCC can compress not only consecutive several groups whose bits are valued 1 (or 0) but also a single group so that a large embedding space is provided. The encryption method including the bit-level XOR-encryption and scrambling operations enhances the security. The experimental results show that the proposed scheme can achieve large embedding capacity and high security.

Reversible data hiding with automatic contrast enhancement for medical images

Recent developments in data hiding technology have made reversible data hiding (RDH) a popular research topic. This paper is focused on medical images and the up-to-date RDH methods, and proposes an automatic contrast enhancement algorithm called the RDHACEM algorithm considering two metrics: larger embedding capacity and better image visual quality. The proposed algorithm separates medical images into regions of interest (ROI) and regions of non-interest (NROI). It automatically stretches the ROI's grayscale histogram to simultaneously enlarge the embedding capacity of the ROI and enhance the image contrast. Experimental results show that the marked medical images generated by the proposed algorithm have better visual quality and larger embedding capacity in the ROI than those generated by the up-to-date RDH methods.