Carrier Image Research Articles

An Effective Multiple-Bit Data Abstraction and Lossless Retrieval without Shifting

Cryptography and Steganography are two major forms of enforcing data privacy, with the former being prevalent. For small payloads, Steganography is preferred, as the data remains concealed and abstracted. The process involves embedding bits onto a carrier image. However, the disadvantage of steganography is that the carrier image gets distorted, which is a major concern when it comes to pixel sensitive images, such as forensic and medical images that aremission critical. Furthermore, due to modifications in the pixel values, the images get distorted, which can be irrecoverable. Hence, the technique of Reversible Data Hiding(RDH) can be used as a solution. Usually two methods are used under RDH, namely Histogram Shifting (HS) and Difference Expansion (DE). Histogram Shifting method shifts all pixel values between pairs of peak and zero points, which allows data to be embedded in the intermediate points. However, this method also depends on frequencies of pixel values on the image, andthe embedding capacity varies greatly on the image. In Difference Expansion Method, the difference between a pixel and its adjacent pixels is considered to embed a single bit intoone of the pixels. Here, a scheme to embed multiple bits in a pixel is proposed, which greatly enhances the embedding capacity. Initially, three horizontally adjacent pixels are takenand the difference between the rightmost and leftmost pixel iscalculated. This difference is used to classify the centre pixel as embeddable or non-embeddable. If the pixel is embeddable, anew pixel value is calculated, which comprises of the secret bits, offset bits and flag bits, without the use of any difference expansion or histogram shifting process. The secret bits and the original pixel value can be easily recovered from the modified value using the offset and flag bits. This scheme has higher embedding capacity, and lower complexity in terms of computations, when comparedwith other RDH schemes.

Robust steganographic framework for securing sensitive healthcare data of telemedicine using convolutional neural network

AbstractData is the key element that runs the modern society. Large amounts of data are being released day by day as a result of many activities. The digital data is transferred through the Internet which may be vulnerable to attacks while transmitting. Especially, the medical data is observed to be of at most importance. Thus, the security of the data should be preserved while communicating through the Internet. Many techniques have been evolved for securing the data, such as Encryption which makes the data not understandable by others and techniques such as Steganography, which hides the data inside other file or document. Traditional methods of Steganography‐like LSB substitution are subjected to risks of manipulation of carrier image and poor embedding capabilities. In light of this, the authors introduce a Convolutional Neural Network‐based image steganography method that operates on DICOM images which conceals the sensitive medical data in the carrier file, thereby increasing the privacy of the sensitive data. The data considered here is brain MRI scan images. The Prep Net, Hiding Net and Reveal Net networks, each with distinct number of convolution layers are used to build the model. The sensitive data is firstly segmented to detect the Region of Interest (ROI) and then the combination of sensitive data and the carrier image are fed to the Prep Net to manipulate sensitive images in such a form that it can easily fit inside the carrier image. The Hiding Net takes the result of Prep Net and embeds the sensitive image inside the carrier image to form a stego image. The Reveal Net takes the result of Hiding Net image and then extracts the sensitive image with minimal loss. The dataset contains medical images of DICOM format taken from open source platform Kaggle that contains 3500 images of 2 classes. The results obtained show that the proposed model achieves 20 percent better output compared to traditional LSB methods. As a result, the potential for hiding the data will be expanded, as well as its security.

Authenticating and securing healthcare records: A deep learning-based zero watermarking approach

Security in medical records is critical to patient privacy and confidentiality. Digital Patient Records (DPR) hold sensitive information that can reveal a patient's health status and history. Their unauthorized access or exposure can lead to severe consequences, including identity theft, discrimination, and medical malpractice. Therefore, ensuring proper security measures is critical in protecting DPR and other medical records from breaches or unauthorized access. In this regard, a robust deep learning-based zero-watermarking approach is presented for authenticating and securing healthcare records. The carrier image is initially visibly marked with the hospital logo to identify ownership and prevent illegal duplication and forgery. The image mark is scrambled by applying the step space-filling curve method for improved security. In the final phase, Alexnet is used to extract the features of visibly marked carrier image. Further, NSST and SVD-based zero watermarking is implemented to conceal the scrambled mark within the features of visibly marked carrier images. It is essential for copyright protection since it establishes ownership while preventing the unauthorized use or dissemination of valuable medical research, images, and reports. The proposed framework has exhibited superior versatility, robustness, and imperceptibility compared to existing techniques with a maximum improvement of 47%.

MIWET: Medical image watermarking using encryption and fusion technique

Digital augmentation of contemporary healthcare has directed the expansion of ICT-based systems. At this stage, a variety of telemedical support has been implemented to ensure the provision of medical utilities. Data Privacy, reliability, and credibility are the three security prerequisites that demand constant monitoring and corrective measures. Watermarking and cryptographic techniques offer a potentially viable solution for effectively addressing the aforementioned challenges. This article presents a robust and secure watermarking procedure that effectively safeguards the privacy and accuracy of digital medical data. This work uses a hybridization of NSST and DTCWT to implement a multimodality image fusion method that generates the watermark image. Further, a combination of three transforms namely, NSST, BEMD, and MSVD is used to conceal the generated mark in the carrier image. Also, the weighting factor is computed using HOG-based optimization to achieve a balance between visual quality and robustness. Finally, the marked carrier image is encrypted with a suitable encryption technique for better security. The proposed framework’s versatility, embedding capacity, robustness, and imperceptibility are evaluated, with the results justifying its efficacy. The study demonstrates a significant improvement of 47.65 % over the baseline, thus highlighting the framework’s superiority.

High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models

With the extensive adoption of generative models across various domains, the protection of copyright for these models has become increasingly vital. Some researchers suggest embedding watermarks in the images generated by these models as a means of preserving IP rights. In this paper, we find that existing generative model watermarking introduces high-frequency artifacts in the high-frequency spectrum of the marked images, thereby compromising the imperceptibility and security of the generative model watermarking system. Given this revelation, we propose an innovative image watermarking technology that takes into account frequency-domain imperceptibility. Our approach abandons the conventional convolutional neural network (CNN) structure typically used as the watermarking embedding network in popular watermarking techniques. This helps the image watermarking system to avoid the inherent high-frequency artifacts commonly produced by CNNs. In addition, we design a frequency perturbation generation network to generate low-frequency perturbations. These perturbations are subsequently added as watermarks to the low-frequency components of the carrier image, thus minimizing the impact of the watermark embedding process on the high-frequency properties of the image. The results show that our proposed watermarking framework can effectively embed low-frequency perturbation watermarks into images and effectively suppress high-frequency artifacts in images, thus significantly improving the frequency-domain imperceptibility and security of the image watermarking system. The introduced approach enhances the average invisibility performance in the frequency domain by up to 24.9% when contrasted with prior methods. Moreover, the method attains superior image quality (>50 dB) in the spatial domain, accompanied by a 100% success rate in watermark extraction in the absence of attacks. This underscores its capability to uphold the efficacy of the protected network and preserve the integrity of the watermarking process. It always maintains excellent imperceptibility and robustness. Thus, the framework shows great potential as a state-of-the-art solution for protecting intellectual property.

Verifiable visually meaningful image encryption algorithm based on compressive sensing and (t, n)-threshold secret sharing

Abstract A verifiable visually meaningful image encryption algorithm based on compressive sensing and (t, n)-threshold secret sharing is proposed. Firstly, the plain image is compressed and encrypted by 2D block compressive sensing to obtain the pre-encrypted image. During this process, the enhanced logistic map and the enhanced tent map are used to generate the measurement matrix and the permutation sequence, respectively. Secondly, multiple shadow images of the pre-encrypted image are generated by using the (t, n)-threshold secret sharing scheme, and then each shadow image is further encrypted by using the encryption matrix generated by the chaotic system. Meanwhile, the signatures of the shadow images are obtained by using RSA signature algorithm. Finally, the shadow images and their corresponding signatures are embedded into the carrier image to generate the cipher image with high visual quality by using the LSB method. Additionally, the present algorithm can resist known-plaintext and chosen-plaintext attacks by incorporating the hash value of the plain image as part of the key. Meanwhile, the use of 2D block compressive sensing significantly reduces the reconstruction time. Simulation results demonstrate that the proposed algorithm achieves excellent decryption quality and operational efficiency.

Using Deep Learning to Embed Dual Marks with Encryption Through 3D Chaotic Map

Several deep learning (DL) based data hiding techniques have been proposed in recent years. Generally, these techniques embed and extract hidden marks using deep learning networks. Although these methods are imperceptible and robust, many algorithms are being demonstrated to embed a single mark, which may incur the risk of transmission insecurity. To solve these problems, this paper proposes a robust and secure data-hiding method using deep learning for embedding dual marks in the form of images within a carrier image file. This has significant advantages in data tracing, data usage monitoring and multiple property management. Further, the proposed framework uses a chaotic system over the marked file to achieve a high level of security. Extensive experimental results demonstrate that our proposed method using deep learning is superior to the state-of-the-art techniques with significant improvement of 42.33% and 41.62% in terms of visual quality and robustness, respectively.

Review on various image protection methods

The security of information exchange over email and other web-based means is rather low, and there is the risk of interception when it comes to confidential information (e.g., credit card info). Therefore, online users should be ensured of the security and privacy of their web interactions. Despite the remarkable benefits of the Internet in the modern era and its contribution to digital communication, the information security of open networks has become an important issue due to its heavy costs. Several approaches have been used to address this issue, particularly through information hiding and encryption in system security strategies. Cryptography is the process of information encryption, and steganography refers to information hiding. These methods have proven quite effective in maintaining information security. Cryptography involves making changes to confidential information, which prevents their readability by eavesdroppers, which could also be considered a technique of original text to cipher text. On the other hand, steganography entirely hides confidential information from unauthorized users using multiple carrier formats, including audio, video, protocol, and images. Given the availability of digital images, they are often employed as carrier files online. Other approaches to image steganography are also common and have specific limitations and advantages. The present study aimed to review different methods of image cryptography and steganography and compare the studies in this regard so as to determine which the best, specific  and accurate methods of information security.

DEVELOPMENT OF NOVEL COMPARISON BASED STEGANOGRAPHY ALGORITHMS ON MULTIMEDIA TO HIDE PRIVATE DATA

Throughout history, humanity has had secrets to safeguard, information that needed to be conveyed to allies in a way that enemies couldn't decipher. To achieve this goal of safeguarding important and valuable information, cryptography and steganography have been frequently used methods both in the past and in today's world. This article introduces a steganographic algorithm designed for hiding data in the color images, along with two different algorithm designs derived from this method. The bits of the hidden message are embedded sequentially into each pixel using the bit comparison. The comparison method works by matching the bits of the image and the message. The least significant bits (LSB) of the carrier (cover) image bytes change depending on the number of matching bits between the carrier image and the hidden message. The proposed method has the potential to hide 1 byte of data within 5 bytes under optimal conditions. The designed algorithms have been tested on a series of color images, and satisfactory results have been achieved in terms of embedding a sufficient amount of data into the images without compromising image quality. The results have been compared with the results of the LSB technique and similar methods based on various performance criteria.

Research on double camouflage encryption mechanism of QR code based on UAV landing scenario

Usually, the landing area of the drone is presented with QR code images, so it is crucial to ensure the information security of the landing area and prevent it from being occupied by other users. This paper proposes a double camouflage encryption method of QR code based on UAV landing scenario. For the QR code image required for UAV landing, the private key and carrier image are used to complete double camouflage encryption, and then the public key is modulated according to the principle of ghost imaging to obtain the ciphertext. After receiving the ciphertext, the receiver first decrypts the camouflage image according to the public key, and then decrypts the QR code image using the private key. The UAV receives ciphertext information through the detector, for non-users, the correct QR code image cannot be decrypted through the wrong key. Even if the eavesdropper obtains the public key information, they can only decrypt the camouflage image and cannot land. For our users, the public key and the double private key can decrypt the correct QR code image for landing. This encryption method can effectively decrypt the image at non-full sampling rate, while also resisting the external noise attack, and has high security.

Robust Reversible Watermarking by Fractional Order Zernike Moments and Pseudo-Zernike Moments

Robust reversible watermarking (RRW) is one of the most popular areas in information hiding. Existing schemes have two drawbacks: 1) schemes that can resist conventional attacks often fail to resist geometric attacks, and 2) schemes that can resist geometric attacks often are not robust against conventional attacks and have poor stability. Inspired by the high robustness of fractional-order orthogonal moments (FoOM) and the good feature of resistance to geometric attacks of Zernike moments and pseudo-Zernike moments (ZM/PZM), in this research, FoOM is used to optimize ZM/PZM to obtain FoZM/FoPZM (namely, fractional-order Zernike moments and fractional-order pseudo-Zernike moments). Furthermore, a denoiser is proposed to preprocess the watermarks to improve the robustness against geometric and conventional attacks, the amount of extracted auxiliary information is decreases and the extraction process of auxiliary information is designed to be more stable. Specifically, first, the source of the difference between the watermarked image and the carrier image is identified, that difference is represented with less information, and then that information is embedded into the cover image as auxiliary information. Second, the watermark is embedded in the low-order FoZM/FoPZM component. Finally, the watermarked image is denoised using a denoiser before extracting the watermark. The experimental results show that the scheme has good stability and strong robustness. Compared with existing methods, the proposed scheme has a small and stable auxiliary information size, strong robustness to noise attacks such as Gaussian noise and salt-and-pepper noise attacks, and better resistance to geometric attacks such as rotation and scaling attacks.

Crypto-space image steganography based on blind flipping

Reversible data hiding (RDH) for digital images is a widely studied steganography that ensures the carrier image can be restored without distortion. Due to the popularity of Internet applications, a lot of research reports focus on the RDH for encrypted images (RDHEI) recently. For a fully encrypted image, data hiding by direct modification of carrier units usually suffers from the problem of error restoration. We propose two mechanisms to solve this problem. The first mechanism is to disperse error units with Arnold transform. The second mechanism is to correct possible errors with Hamming coding. By incorporating both mechanisms, the error restoration rate can be greatly reduced. Besides, the periodicity of Arnold transform is also investigated. By leveraging a full search program and the frequency analysis, the maximum periods together with their parameters for some practical matrix sizes are found. This information is beneficial to get desired matrix state with Arnold Transform. Ninety-eight grayscale images from USC-SIPI and BOSSbase v1.01 image datasets were tested. Due to the reduction of error restoration rate, the proposed mechanisms successfully raise the data payload from 0.1177 to 0.172 bits per pixel.

A visually secure image encryption method based on semi-tensor product compressed sensing and IWT-HD-SVD embedding

The conventional approach for images encryption entails transforming a regular image into an encrypted image that resembles noise. However, this noise-like encrypted image is susceptible to drawing the attention of an attacker when transmitted through a public channel. Hence, there has been a recent surge in the interest of academics towards visually secure image encryption techniques. In a broad sense, encryption methods that include visual significance should prioritize four key elements: the resemblance between the cypher picture and the carrier image, the capacity for embedding, the attainment of good recovery quality, and resilience against many forms of attacks. To address the issues pertaining to inadequate visual security, limited resistance against attacks, and subpar quality of reconstructed images observed in contemporary image encryption and compression methodologies. This paper proposes a visually secure image encryption method based on improved semi-tensor product compressed sensing, two-way cross zigzag obfuscation, and IWT-HD-SVD embedding. Firstly, the plain image is sparsely represented in the Discrete Wavelet Transform (DWT) domain, and a two-way cross zigzag mismatch strategy is proposed to disarrange the coefficient vectors. Then the plain image is encrypted as a secret image by the improved semi-tensor product compression sensing technique. After that, IWT-HD-SVD embedding technique is proposed to embed the secret image into the carrier image to generate the final meaningful cryptographic image. This dramatically improves the visual security of the cryptographic image. Simulation results show that the quality of the decrypted image is approximately 36 dB and up to 44 dB. In addition, the cryptographic image is highly robust against common noise attacks of 0.05 %.

PVO-based reversible data hiding using bit plane segmentation and pixel expansion

The existing pixel value ordering (PVO)-based reversible data hiding (RDH) approaches have been investigated; however, the functions of these techniques are weak when embedded in rough image regions. Therefore, a novel PVO-RDH method is proposed herein. This method performs ‘bit-plane segmentation low bit-plane weighted-pixel expansion’ on an image, which improves the original PVO-RDH method’s embedding performance in rough image regions. Pixel expansion increases the number of image chunks, thereby improving the maximum embedding capability of the carrier image. After preprocessing, the expanded picture is chunked using the original PVO-RDH strategy, and the maximum value in the chunk is modified via prediction. In contrast to the results of previous studies, the current experimental findings indicate that the proposed strategy improves the average PSNR by at least 2.97 dB when the embedding data are 10000 bit. The experimental findings show a significant improvement in the embedding volume and PSNR of the proposed algorithm.

EmbedR-Net: Using CNN to Embed Mark With Recovery Through Deep Convolutional GAN for Secure eHealth Systems

The goal of security is to protect digital assets, devices and services from being disrupted, exploited or stolen by unauthorised users. It is also about having reliable media information available at the right time. However, the media distortion will pose many potential risks in eHealth systems. In this paper, a new data hiding method called EmbedR-Net based on Convolutional Neural Network (CNN) and Deep Convolutional Generative Adversarial Network (DCGAN) is proposed, which can prevent the copyright violation of the medical images. First, a CNN based embedder network is designed for imperceptibly hiding medical images as marks in the carrier image. Second, we compute the diagonal value of the marked image for hidden mark recovery. Last, the DCGAN network is designed to robustly recover the hidden mark using the diagonal value of the marked image. Compared to existing methods, experiments on five different datasets have shown that the proposed EmbedR-Net obtains superior performance.

Quantum color image watermarking scheme based on quantum error correction coding * *This work was supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS–202208), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJZD-M202000501) and National Natural Science Foundation of China (No. 61772295).

Quantum image processing, which merges classical image processing techniques with quantum computing, provides exceptional storage capacity and unparalleled parallel computing power. In this study, we present a quantum color image watermarking scheme that employs quantum error correction codes to address issues such as pixel loss and image distortion during watermark embedding and extraction. By utilizing the least significant bit method to embed the color values of the watermark image into those of the carrier image, we improve the scheme’s robustness. We also address the error correction capabilities of channel coding for phase-flip errors and follow the majority principle, resulting in more accurate extraction of the watermark image’s color and enhancing the watermarking scheme’s reliability and integrity. Our experimental simulations demonstrate that the proposed watermarking scheme boasts high security, strong robustness, and excellent concealment.

Quality guided reversible data hiding with contrast enhancement

Reversible data hiding with contrast enhancement (RDH-CE) enables contrast enhancement of the image and lossless recovery of host image after embedded data extraction. Although the current RDH-CE methods are effective in enhancing carrier images and ensuring reversibility, image quality control is still lacking, which may lead to poor image visualization. In more serious situations, the image detail information may be lost. The peak pair selection mechanism plays an extremely crucial role in image quality enhancement; however, existing work often neglects this. In this paper, we propose an RDH-CE algorithm that pursues high image quality maintenance, aiming to maintain image quality by guiding and controlling the selection of peaks and the direction of histogram shift through quality metrics. Specifically, instead of simply selecting only one pair of peaks at each peak selection stage, our algorithm selects several pairs of peaks that meet the requirements as candidate peak pairs. It then first performs simulated histogram equalization on these candidate peaks, followed by analyzing the situation of the quality metrics generated by each pair of peaks accordingly, and then selects the optimal peaks for the current iteration based on the performance of each pair of candidate peaks to be used in the equalization process. Our selection mechanism also dynamically changes the peak selection strategy based on before-and-after changes in the actual metrics, which can better ensure image quality stability over the iterations. Under the evaluations of a series of image quality metrics, the proposed method can still maintain high quality and visual effect under high iteration and has strong stability for image quality control, thus demonstrating the effectiveness and superiority of the proposed method.

Adaptive embedding combining LBE and IBBE for high-capacity reversible data hiding in encrypted images

Reversible data hiding in encrypted image (RDHEI) has received wide attention as an effective information protection technique, and most of the existing RDHEI schemes introduce pixel prediction technique to improve embedding performance. However, the high embedding capacity (EC) requirement is still not achieved because the redundancy of carrier medium is not fully utilized. Thus, a novel RDHEI based on pixel prediction and adaptively combining "L"-shaped block embedding (LBE) and improved binary-block embedding (IBBE) is presented, which maximizes the use of Laplacian-like distribution of prediction-errors to achieve higher EC. Specifically, the original image is first predicted to get the prediction-error image and then eight binary prediction-error bit-planes (PEBPs) are generated. Subsequently, the proposed LBE and IBBE are used to calculate the net EC to encode each PEBP. Also, a new bit-plane selection encryption is designed to ensure that the intra-block correlations of the IBBE-based PEBPs remain unchanged. Eventually, the data hider adaptively completes data embedding according to the PEBP coding. Moreover, the 4-D hyperchaotic system is exploited to encrypt the carrier image and the secret data to further enhance the security. Extensive experimental results verify that our strategy achieves more outstanding performance compared with some state-of-the-art schemes.

Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform. Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

Exploiting one-dimensional exponential Chebyshev chaotic map and matching embedding for visually meaningful image encryption

As a result of their prominent initial value sensitivity, pseudo-randomness, and unpredictability nature, chaotic maps are widely used in multimedia security. However, the majority of chaotic maps now in use have issues like narrow parameter scope, poor traversability, and insufficient chaotic performance. To address these issues, a one-dimensional exponential Chebyshev (1-DEC) chaotic map is constructed in this paper which performs satisfactorily. Through the use of a bifurcation diagram, Lyapunov exponents, sample entropy, and permutation entropy, the chaotic dynamics of the 1-DEC are thoroughly examined. Subsequently, a visually meaningful image encryption (VMIE) scheme combined with matching embedding based on 1-DEC was presented. Specifically, a cyclic shift confusion method across rows and columns (CSCARC) is proposed to eliminate the correlation of adjacent pixels. Then, an adaptive embedding method based on image energy and dynamic matching (AEIEDM) is proposed to fuse the privacy information into carrier images. In addition, secret image share and authentication information is introduced in this scheme, which improves disaster-tolerance performance and implements the distributed management and storage of data. Subscribers have the capabilities of authentication, management, and data integrity checking. Ultimately, the simulation results and analysis demonstrate that the proposed scheme possesses satisfactory performance in terms of security and robustness, and the visual quality of the steganographic images was above 50.9 dB.