Image Watermarking Scheme Research Articles

A Complex Network Analysis of Image Watermarking Scheme Based on SVD and DWT

A Complex Network Analysis of Image Watermarking Scheme Based on SVD and DWT

Color image watermarking using vector SNCM-HMT

An image watermarking scheme is typically evaluated using three main conflicting characteristics: imperceptibility, robustness, and capacity. Developing a good image watermarking method is challenging because it requires a trade-off between these three basic characteristics. In this paper, we proposed a statistical color image watermarking based on robust discrete nonseparable Shearlet transform (DNST)-fast quaternion generic polar complex exponential transform (FQGPCET) magnitude and vector skew-normal-Cauchy mixtures (SNCM)-hidden Markov tree (HMT). The proposed watermarking system consists of two main parts: watermark inserting and watermark extraction. In watermark inserting, we first perform DNST on R, G, and B components of color host image, respectively. We then compute block FQGPCET of DNST domain color components, and embed watermark signal in DNST-FQGPCET magnitudes using multiplicative approach. In watermark extraction, we first analyze the robustness and statistical characteristics of local DNST-FQGPCET magnitudes of color image. We then observe that, vector SNCM-HMT model can capture accurately the marginal distribution and multiple strong dependencies of local DNST-FQGPCET magnitudes. Meanwhile, vector SNCM-HMT parameters can be computed effectively using variational expectation–maximization (VEM) parameter estimation. Motivated by our modeling results, we finally develop a new statistical color image watermark decoder based on vector SNCM-HMT and maximum likelihood (ML) decision rule. Experimental results on extensive test images demonstrate that the proposed statistical color image watermarking provides a performance better than that of most of the state-of-the-art statistical methods and some deep learning approaches recently proposed in the literature.

Bivariate BMM-based hybrid domain image watermark detector

Robustness, imperceptibility, and watermark capacity are three indispensable and contradictory properties for any image watermarking systems. It is a challenging work to achieve the balance among the three important properties. In this paper, by using the bivariate Beta mixture model (Bivariate BMM), we present a statistical image watermark scheme in nonsubsampled Contourlet transform (NSCT)-fractional order Jacobi Fourier moments (FJFMs) amplitude hybrid domain. The whole watermarking algorithm includes two parts: watermark embedding and detection. NSCT is firstly performed on host image to obtain the frequency subbands, and the NSCT subbands are divided into non overlapping blocks. Then, the significant NSCT domain blocks are selected using local binary patterns (LBP). Meanwhile, for each selected NSCT coefficient block, FJFMs are calculated to obtain the NSCT-FJFMs amplitude. Finally, watermark signals are inserted into the amplitude hybrid domain of NSCT-FJFMs. In order to detect accurately watermark signal, the statistical characteristics of NSCT-FJFMs magnitudes are analyzed in detail. Then, NSCT-FJFMs magnitudes are described statistically by Bivariate BMM, which can simultaneously capture the marginal distribution and strong dependencies of NSCT-FJFMs magnitudes. Also, Bivariate BMM parameters are estimated accurately by the rough-enhanced-Bayes mixture estimation & expectation–maximization (REBMIX&EM) approach. Finally, a statistical watermark detector based on the locally optimum (LO) decision rule and Bivariate BMM is developed in NSCT-FJFMs magnitude hybrid domain. Also, the closed-form expressions on the LO statistic is derived and the receiver operating characteristic (ROC) about our detector is analyzed. Extensive experimental results show the superiority of the proposed image watermark detector over some state-of-the-art statistical watermarking methods.

A reversible-zero watermarking scheme for medical images

The paper addresses the issue of ensuring the authenticity and copyright of medical images in telemedicine applications, with a specific emphasis on watermarking methods. While several systems only concentrate on identifying tampering in medical images, others also provide the capacity to restore the tampered regions upon detection. While several authentication techniques in medical imaging have successfully achieved their goals, previous research underscores a notable deficiency: the resilience of these schemes against unintentional attacks has not been sufficiently examined or emphasized in previous research. This indicates the need for further development and investigation in improving the robustness of medical image authentication techniques against unintentional attacks. This research proposes a Reversible-Zero Watermarking approach as a solution to address these problems. The new approach merges the advantages of both the reversible and zero watermarking techniques. This system is comprised of two parts. The first part is a zero-watermarking technique that uses VGG19-based feature extraction and watermark information to establish an ownership share. The second part incorporates this ownership share into the image in a reversible manner using a combination of a discrete wavelet transform, an integer wavelet transform, and a difference expansion. Research findings confirm that the suggested watermarking approach for medical images demonstrates substantial enhancements compared to current methodologies. Research findings indicate that NC values are often around 0.9 for different attacks, whereas BER values are close to 0. It demonstrates exceptional qualities in being imperceptible, distinguishable, and robust. Additionally, the system provides a persistent verification feature that functions independently of disputes or third-party storage, making it the preferred choice in the domain of medical image watermarking.

A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping

A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping

Image watermarking based on a ratio of DCT coefficient sums using a gradient-based optimizer

Social networks and various websites provide a lot of tools for publishing and sharing digital images. However, publishing digital content online makes it vulnerable to illegal copying and distribution. An effective method for protecting images is watermarking technology, which embeds a logo or some kind of identifier into an image in an invisible way. If necessary, a watermark can be extracted from an image and used to prove authorship and ownership. A major challenge in designing image watermarking schemes is balancing embedding imperceptibility and resistance to various attacks. In this study, we propose a new robust and blind image watermarking algorithm that set a specific ratio of sum of discrete cosine transform (DCT) coefficient absolute values for each image block. We provide a balance between imperceptibility and robustness using a modern metaheuristic optimization algorithm called gradient-based optimizer, which finds the best vector of changes for mid-frequency DCT coefficients. An objective function of optimization algorithm is designed in such a way as to control the ratio of the sums of DCT coefficients absolute values to increase robustness and reduce a distortion level of an image block during embedding. Experimental results and comparison with some other image watermarking schemes demonstrate the high efficiency of the proposed algorithm in terms of imperceptibility and resistance to typical image processing attacks, such as changing brightness and contrast, sharpening, cropping, and others.

Image watermarking scheme employing Gerchberg–Saxton algorithm and integer wavelet transform

Image watermarking scheme employing Gerchberg–Saxton algorithm and integer wavelet transform

A Reversible Medical Image Watermarking Scheme for Advanced Image Tampering Detection

A Reversible Medical Image Watermarking Scheme for Advanced Image Tampering Detection

MuST: Robust Image Watermarking for Multi-Source Tracing

In recent years, with the popularity of social media applications, massive digital images are available online, which brings great convenience to image recreation. However, the use of unauthorized image materials in multi-source composite images is still inadequately regulated, which may cause significant loss and discouragement to the copyright owners of the source image materials. Ideally, deep watermarking techniques could provide a solution for protecting these copyrights based on their encoder-noise-decoder training strategy. Yet existing image watermarking schemes, which are mostly designed for single images, cannot well address the copyright protection requirements in this scenario, since the multi-source image composing process commonly includes distortions that are not well investigated in previous methods, e.g., the extreme downsizing. To meet such demands, we propose MuST, a multi-source tracing robust watermarking scheme, whose architecture includes a multi-source image detector and minimum external rectangle operation for multiple watermark resynchronization and extraction. Furthermore, we constructed an image material dataset covering common image categories and designed the simulation model of the multi-source image composing process as the noise layer. Experiments demonstrate the excellent performance of MuST in tracing sources of image materials from the composite images compared with SOTA watermarking methods, which could maintain the extraction accuracy above 98% to trace the sources of at least 3 different image materials while keeping the average PSNR of watermarked image materials higher than 42.51 dB. We released our code on https://github.com/MrCrims/MuST

An image watermarking scheme: combining the transform domain and deep learning modality with an improved scrambling process

ABSTRACT This paper proposes a novel image watermarking scheme with the following two processes. In the embedding process, the cover image is subjected to NSCT to obtain the properties of multi-scale and directional characteristics and fed to DWT which decomposes the contourlet image into various sub-bands. The wavelet decomposition image is subjected to a region selection step, where the regions are selected based on the DL strategy for selecting the region from the contourlet of the cover image where the features, including LOOP feature, mean feature, Canny features, and ResNet features are extracted and subjected to the DNN classifier. Conversely, the watermark image performs NSCT and DWT as the cover image and is fed into further steps to eliminate the correlation of pixels. For that, it proposes the FTTS approach to scramble the contourlet of watermark images, in which tent map and dyadic transformation are employed to eliminate the correlation among pixels. Then the resultants of region selection and FTTS are embedded to obtain the watermarked image. On the other hand, the extraction process takes place, which extracts the embedded watermarked image and applies inverse scrambling and inverse an NSCT approach to recover the watermark image against any malicious attack.

Color Image Watermarking Using Adaptive Tunable Q Wavelet, Finite Ridge Let, and Arnold Transforms with TSVD

In recent years, researchers have been working to integrate watermarking practices anchored in wavelet transform (WT) coefficients and Singular value decomposition (SVD) to recover the robustness of watermarked images in opposition to geometric and non-geometric attacks. Conversely, the watermark embedding, can initiate vagueness in the false-positive problem (FPP). To address this issue, Flamingo search algorithm-based Color Image Watermarking using Adaptive Tunable Q wavelet, Finite ridge let, Arnold transforms with Truncated Singular value decomposition (ATQWT-FRT-AT-TSVD-FSOA-CIW) is proposed. The proposed ATQWT-FRTAT-TSVD-FSOA-CIW method is separated into two stages: (i) watermark embedding and (ii) watermark extraction. The scaling factor is optimized with the help of the Flamingo search algorithm, which provides higher robustness, higher embedding strength, resistance to a false-positive problem, and better quality of the watermarking scheme. The proposed ATQWT-FRT-AT-TSVD-FSOA-CIW method is simulated in MATLAB tool. The performance of the proposed ATQWT-FRT-AT-TSVD-FSOA-CIW design is estimated under several performance metrics, such as Peak signal to noise ratio (PSNR), Structural similarity index measurement (SSIM), and Normalized cross-correlation (NCC). The performance of the proposed ATQWT-FRT-AT-TSVD-FSOA-CIW method attains 35%, 22.43%, 21.157%, 13.96%, and 7.186% higher PSNR compared to the existing designs, like secure with robust color image watermarking utilizing nature-inspired intelligence (DWT-SVD-ABCOA-CIW), the Artificial bee colony basis perceptually tuned blind color image watermarking in the hybrid LWT-DCT domain (LWT-DCT-ABCOA-CIW), the DWT-SVD based adaptive color multi-watermarking scheme for copyright protection under AMEF and PSO-GWO (DWT-SVD-Hybrid PSO-GWOA-CIW), Robust image watermarking based on fractional Krawtchouk transform with optimization (FrKT-Hybrid FO-CSOA-CIW), and the optimized semi-blind scheme for color image watermarking (DWTFRT-AT-SVD-PSO-CIW).

Paired pixels mean-based near reversible watermarking scheme for digital images

Paired pixels mean-based near reversible watermarking scheme for digital images

A watermarking scheme for color images that achieves optimality using the Transit Search Algorithm

This paper describes an innovative watermarking method that combines discrete wavelet transform (DWT), Hessenberg decomposition (HD), and singular value decomposition (SVD). To do this, the main image and the watermark are divided into three channels (red, green and blue – RGB). Then, each part of the main image individually undergoes the steps of DWT, HD and SVD, while the watermark components are processed by SVD. Insertion of the watermark is carried out by adjusting the singular values of the watermark and the main image, using a watermark scaling factor (α). The optimal choice of α poses a challenge, so the transit search algorithm is employed to find a trade-off between visibility and robustness. To evaluate this method, comparisons are made with other studies using various optimization algorithms such as particle swarm optimization, artificial bee colony and fly optimization algorithm. The results of the experiments confirm the effectiveness of this technique.

基于量子漫步和像素分块算法的量子彩色图像水印方案

基于量子漫步和像素分块算法的量子彩色图像水印方案

In-depth exploration of digital image watermarking with discrete cosine transform and discrete wavelet transform

Digital image watermarking is a crucial technique used to protect the integrity and ownership of digital images by embedding imperceptible watermarks into the image content. This review concentrates on the utilization of discrete cosine transform (DCT) and discrete wavelet transform (DWT) in digital image watermarking schemes. DCT, widely used in image compression like JPEG, is an attractive choice for watermarking, modifying DCT coefficients with minimal impact on image quality. On the other hand, DWT offers multiresolution representation, enabling better localization and robustness against attacks. DWT-based methods use wavelet coefficients to embed watermarks in specific frequency bands or image regions. The review examines the strengths and weaknesses of DCT and DWT in digital image watermarking, exploring algorithms and approaches proposed in the literature. It also addresses challenges like attacks, synchronization, and robustness to image processing. Additionally, a comparative analysis of DCT and DWT-based methods considers imperceptibility, robustness, capacity, and computational complexity. By offering valuable insights, this review aids researchers and practitioners in implementing secure and efficient digital image watermarking solutions.

A Hybrid DWT-SVD Based Adaptive Image Watermarking Scheme

In the digital age, protecting the ownership and data veracity of digital documents is a major challenge. To address the issues concerning copyright protection and data verification of digital media, digital watermarking has emerged as a solution. In this paper, we aspire to make a modest contribution to this emerging and exciting field by presenting our proposed adaptive hybrid image watermarking approach that combines Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). Our method involves applying DWT to both the host image and watermark, followed by singular decomposition using SVD on the Low-Low (LL) component of both images. Now modify the singular values of the host image by the singular values of the watermark, and then inverse SVD is applied, followed by inverse DWT, to obtain the watermarked image. After that, the reverse process is applied to obtain the watermark image. Finally, we evaluate our approach’s performance by measuring the Peak Signal-to-Noise Ratio (PSNR) between the original and watermarked image as well as the Normalized Cross-Correlation (NCC) between the original and extracted watermark. Simulation results indicate that the proposed method is rich in terms of robustness, imperceptibility and capacity than the previously presented schemes.

Blind watermarking scheme for medical and non-medical images copyright protection using the QZ algorithm

The widely used watermarking technique is to hide the watermark information through adding process, which means the watermark is added with the host image to realize embedding. The constraints of these addition-based watermarking systems mainly are non-blind, they are vulnerable to forgery attacks and have the false-positive problem. To solve these problems, a novel blind watermarking scheme based on the QZ algorithm for medical and normal images is first presented in this paper. In the presented scheme, we take the watermark and the host image as two inputs of the QZ algorithm to realize embedding, rather than the adding process. The outputs of the QZ algorithm include four parts: two upper quasitriangular matrices and two unitary matrices. Then the three parts of the outputs are synthesized by the vector synthesis and inverse discrete wavelet transform-singular value decomposition (DWT-SVD) to obtain the watermarked host image. Due to the characteristics of the QZ algorithm, our scheme is blind. The performance is tested and compared by applying our scheme to both medical and non-medical images. The experimental results show that our scheme is robust against various image-processing attacks, forgery attacks, and false-positive problem. And from the comparison results, the proposed scheme outperforms the existing addition-based schemes in terms of robustness against the Median filter, Low-pass filter, Gaussian blur attacks, and forgery attacks, while maintaining good invisibility.

A DFT and IWT-DCT Based Image Watermarking Scheme for Industry

A DFT and IWT-DCT Based Image Watermarking Scheme for Industry

Large-capacity fragile watermarking scheme for color images based on prime number distribution theory

Large-capacity fragile watermarking scheme for color images based on prime number distribution theory

High imperceptibility and robustness watermarking scheme for brain MRI using Slantlet transform coupled with enhanced knight tour algorithm

This research introduces a novel and robust watermarking scheme for medical Brain MRI DICOM images, addressing the challenge of maintaining high imperceptibility and robustness simultaneously. The scheme ensures privacy control, content authentication, and protection against the detachment of vital Electronic Patient Record information. To enhance imperceptibility, a Dynamic Visibility Threshold parameter leveraging the Human Visual System is introduced. Embeddable Zones and Non-Embeddable Zones are defined to enhance robustness, and an enhanced Knight Tour algorithm based on Slantlet Transform shuffles the embedding sequence for added security. The scheme achieves remarkable results with a Peak Signal-to-Noise Ratio (PSNR) evaluation surpassing contemporary techniques. Extensive experimentation demonstrates resilience to various attacks, with low Bit Error Rate (BER) and high Normalized Cross-Correlation (NCC) values. The proposed technique outperforms existing methods, emphasizing its superior performance and effectiveness in medical image watermarking.