Multiple Watermarking Research Articles

A DWT-SVD based adaptive color multi-watermarking scheme for copyright protection using AMEF and PSO-GWO

Color multi-watermarking is a challenge in the field of copyright protection, and the key in the color multi-watermarking technology is how to determine the optimal embedding regions and embedding strengths to achieve the trade-off among multiple watermarks while maintaining great invisibility, sufficient robustness, and large capacity. In order to tackle these problems, an adaptive multiple embedding factors (AMEF) algorithm for calculating the optimal embedding regions and the optimal embedding strengths is proposed in this paper to embed multiple color watermarks simultaneously. In the presented AMEF algorithm, we propose that the optimal embedding regions are determined by the contrast function of different blocks. Then we determine the multiple embedding strengths to depend on the weighted ratio of the contrast values of blocks and the eigenvalues of different marks. Furthermore, in order to calculate the weight value in the AMEF algorithm, we define a single objective function and utilize a hybrid particle swarm optimization and grey wolf optimizer (PSO-GWO) algorithm to optimize the objective function. In this work, by the use of the discrete wavelet transform (DWT), singular value decomposition (SVD) and AMEF, four encrypted color watermarks are inserted into the selected regions of the color (normal or medical) host image, simultaneously. Then watermarked host image is tested under various attacks and compared to other recent existing schemes. The experimental results demonstrate that the proposed scheme can effectively achieve the trade-off among the invisibility, robustness and capacity, simultaneously. And from the comparison results, the proposed scheme possesses high security, large capacity, and strong robustness against various attacks while maintaining good invisibility.

Wavelet Transform based Multiple Image Watermarking Technique

In satellite communication the information’s like frequency, Polarization, Data format, Timing information are need to be protected for secure communication. Digital watermarking is the technique which provides secure way of communication in which the secret information or watermark can be transmitted safely. Wavelet Transform is playing an increasingly important role in watermarking due to its good spatial-frequency characteristics, and its wide applications in the image and video coding standards. In this work a novel Hybrid Steerable Pyramid with Discrete Wavelet Transform (SP+DWT) based multiple watermarking is attempted for colour images. In order to increase the robustness of the algorithm Arnold transform and Fibonacci transform based image scrambler is applied to watermarks before embedding in to cover image. The performance of the proposed scheme is validated for various attacks, such as Gaussian noise, Salt & Pepper Noise, Rotation, Translation and Speckle Noise. The proposed method is withstood for all the noise attacks and the performance of the proposed algorithm is measured using Peak Signal to Noise Ratio (PSNR), Normalized Correlation (NC) and Similarity Measure (SM). Pearson Independent component Analysis (ICA) is applied at the receiving end to extract watermarks from the input image. To analyse the inclusion of image scrambler the results of proposed algorithm is compared with same algorithm without image scrambler applied. The performance of proposed technique is also compared with other wavelet transforms like Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT) and Integer Wavelet Transform (IWT). Simulation results such as PSNR,NC,SM shows that hybrid SP+DWT based watermarking technique and extraction using Pearson ICA results in high imperceptible, better security and robustness, when compared to DWT, WPT and IWT based watermarking and extraction using Pearson ICA.

Blind multipurpose watermarking with insertion of a single watermark: a generic construction based on verifiable threshold secret sharing

Currently, the existing literature on multipurpose watermarking employs multiple watermarks for simultaneous achieving of multiple security objectives. This approach can be problematic since a watermark targeting tampers might fail to detect tampering of another watermark inserted for copyright violation. Moreover, most of the current schemes follow a non-blind approach where the original watermark is needed for authentication. A question that naturally arises is whether multipurpose watermarking can be realised with the insertion of a single watermark in a blind way or not. The goal of the authors study is to provide an affirmative answer to this important question. They show how a cryptographic primitive called verifiable threshold secret sharing can be used to come up with a generic construction for blind multipurpose watermarking which inserts a single watermark into the host image for simultaneous achieving of copyright protection, authentication, and tamper localisation. The generic property of the proposed scheme provides flexibility for choosing the embedding/extraction of the watermark based on the desired level of fidelity, robustness and capacity. Experimental results are provided to confirm the superiority of their proposed technique as compared to existing approaches.

A new colour image copyright protection approach using evolution-based dual watermarking

ABSTRACT Multiple watermarking techniques for images is receiving more attention in recent years for its wide variety of applications in different fields such as piracy of digital data, and copyrights protection. Current approaches rely on adding many watermarks in different bands or channels utilising scaling factor, and embedding locations that are mainly defined by experts. This brought many challenges in achieving equilibrium between security, robustness, and quality. Aiming to fill the gap of determining stationary scaling factor and embedding locations, the work proposed in this paper adapts genetic algorithm to find the optimality of these issues; that will enhance both of watermarking capacity and imperceptibility in colour images. To increase security, the suggested model encodes the watermarks using 2-D Walsh transform (WHT). Furthermore, Singular Value Decomposition (SVD) as a dimension reduction tool is utilised to factorise Wavelet coefficients to determine the most salient embedding locations. Experimental results show that the proposed model is more robust against common image manipulation attacks in terms of PSNR and NCC.

Contourlet domain locally optimum image watermark decoder using Cauchy mixtures based vector HMT model

Digital image watermarking has become a necessity in many applications such as data authentication, broadcast monitoring on the Internet and ownership identification. Various watermarking schemes have been proposed to protect the copyright information. There are three indispensable, yet contrasting requirements for a watermarking scheme: imperceptibility, robustness and payload. Therefore, a watermarking scheme should provide a trade-off among these requirements from the information-theoretic perspective. Generally, in order to enhance the imperceptibility, robustness and payload simultaneously, the human visual system (HVS) and the statistical properties of the image signal should be fully taken into account. The statistical model-based transform domain multiplicative watermarking scheme embodies the above ideas, and therefore the detection and extraction of the multiplicative watermarks have received a great deal of attention. The performance of a statistical model-based watermark detector or decoder is highly influenced by the accuracy of the statistical model itself and the applicability of decision rule. In this paper, we firstly propose a new hidden Markov trees (HMT) statistical model in Contourlet domain, namely Cauchy mixtures-based vector HMT (vector CMM–HMT), by describing the marginal distribution with Cauchy mixture model (CMM) and grouping Contourlet coefficients into a vector, which can capture both the subband marginal distributions and the strong dependencies across scales and orientations of the Contourlet coefficients. Then, by modeling the Contourlet coefficients with vector CMM–HMT and employing locally most powerful (LMP) test, we develop a locally optimum image watermark decoder in Contourlet domain. We conduct extensive experiments to evaluate the performance of the proposed blind watermark decoder, in which encouraging results validate the effectiveness of the proposed technique, in comparison with the state-of-the-art approaches recently proposed in the literature.

A Switching Multiplicative Watermarking Scheme for Detection of Stealthy Cyber-Attacks

This article addresses the detection of stealthy attacks on sensor measurements. Inspired in authentication schemes with weak cryptographic guarantees, we propose a watermarking approach to validate the data and its source. In particular, we propose a multiplicative scheme, where the sensor outputs are watermarked by a bank of filters, then transmitted through the possibly unsecured communication network. The original measurement data are finally reconstructed by a watermark remover. To allow the detection of replay attacks, the watermarking filters are devised as hybrid switching systems, whose parameters are assumed to be unknown to the adversary. Design rules are provided, guaranteeing that the nominal closed-loop performance is not deteriorated by the watermarking scheme and ensuring robust stability with mismatched filter parameters. Moreover, we design a switching protocol with no communication overhead to allow the watermarking filters to synchronously update their parameters. The detectability properties of cyber-attacks are analyzed, and the results are illustrated through numerical examples for replay and data injection attacks.

Multiple watermarking technique using optimal threshold

<span>The multiple watermarking technique has drawn more attention due to high demand for embedding more than one copyright. This paper proposes a multiple watermarking scheme using DWT-SVD by utilizing HVS characteristics. Our scheme embeds multiple watermarks in red and blue colors. The proposed scheme examines the coefficients of U orthogonal matrix for embedding and extracting watermarks. Watermarks are scrambled by Arnold transform before embedded in the host image in order provide additional security. The optimal thresholds for red and blue colors are revealed by finding a trade-off between normalized-cross correlation and imperceptibility from quantization steps. The experimental results demonstrate that our scheme achieves high resistant under JPEG and JPEG2000 compression for both inserted watermarks. </span>

Medical image watermarking for ownership & tamper detection

Image watermarking can provide ownership identification as well as tamper protection. Transform domain based image watermarking has been proven to be more robust than the spatial domain watermarking against different signal processing attacks. On the other hand, tamper detection is found to be working well in spatial domain. In the proposed work, the focus is on the improvement of the medical image watermarking by incorporating the concept of multiple watermarking of the host image. The principal components (PC) based insertion make the scheme secured towards ownership attack. On the other hand, LZW (Lempel–Ziv–Welch) based fragile watermarking is used to hide compressed image’s ROI (region of interest) to tackle the intentional tampering attacks. The ROI based watermark generation provides the complete reversibility of the ROI. In this way, proposed scheme provides perfect reversibility of ROI, good imperceptibility in addition to satisfactory robustness. The tamper handing ability of proposed scheme is also tested against various attacks, which turns out to be quite good. The proposed scheme is found to be more useful, when compared with recently proposed schemes in term of features and usefulness.

Robust and Blind Multiple Image Watermarking Using CNN and DWT in Video

Digital watermarking was introduced as a result of rapid advancement of networked multimedia systems. It had been developed to enforce copyright technologies for cover of copyright possession. Due to increase in growth of internet users of networks are increasing rapidly. It has been concluded that to minimize distortions and to increase capacity, techniques in frequency domain must be combined with another technique which has high capacity and strong robustness against different types of attacks. In this paper, a robust multiple watermarking which combine Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT)and Convolution Neural Network techniques on selected middle band of the video frames is used. This methodology is considered to be robust blind watermarking because it successfully fulfills the requirement of imperceptibility and provides high robustness against a number of image-processing attacks such as Mean filtering, Median filtering, Gaussian noise, salt and pepper noise, poison noise and rotation attack. The proposed method embeds watermark by decomposing the host image. Convolution neural network calculates the weight factor for each wavelet coefficient. The watermark bits are added to the selected coefficients without any perceptual degradation for host image. The simulation is performed on MATLAB platform. The result analysis is evaluated on PSNR and MSE which is used to define robustness of the watermark that means that the watermark will not be destroyed after intentional or involuntary attacks and can still be used for certification. The analysis of the results was made with different types of attacks concluded that the proposed technique is approximately 14% efficient as compared to existing work.

FCM-Based Approach for Locating Visible Video Watermarks

The increased usage demand for digital multimedia has induced significant challenges regarding copyright protection, which is the copy control and proof of ownership. Digital watermarking serves as a solution to these kinds of problems. Among different types of digital watermarking, visible watermarking protects the copyrights effectively, since the approach not only prevents pirates but also visually proves the copyright of the broadcasted video. A visible watermark could be in any location on the frame (corner, center, diagonal, etc.). In addition, it could either completely or partially disappear for some frames. The same video also might have multiple watermarks. In order to strengthen the techniques of adding visible watermarks, there is a need to discover the weakness of the used watermarks. Since the major step of attacking a visible watermark is to locate it accurately, in this paper, a Fuzzy C-Means (FCM)-based approach is proposed to locate visible watermarks in video. Broadcasting channels are used to utilize video logos, which can be considered as a form of visible watermark that represents a trademark or symbol to declare the intellectual property ownership. In general, a high-standard video watermark has such properties as a clear background with distinctive shape without additional texture obscuring the watermark area. In addition, the probability of the logo appearing in the four corners of the video frames is higher than in the center. Based on these common properties of the video watermark, the proposed scheme locates the visible watermark using the Fuzzy C-Means technique without any prior information. The proposed technique has two stages: the first stage is positioning, and the second is masking (extracting the watermark mask). Due to real-world limitations such as noise, shadowing, and variations in cameras, the positioning stage is developed by employing gradient and Fuzzy C-Means classifier techniques. By using the dilation and erosion operators, the masking stage is developed to extract the watermark mask. Using a set of trademark videos, the proposed algorithm is tested and evaluated. A comparative study shows that the proposed FCM-based technique is able to achieve higher accuracy at a reasonable computational cost in comparison to the most related and recent published work. The proposed technique could locate different watermarks with high symmetry in their pattern, even if they appeared mutually in the same location. Still, it will be a challenge if the symmetry is low between used watermarks in the same location.

A blind watermark algorithm in SWT domain using bivariate generalized Gaussian distributions

Imperceptibility, robustness, and payload are three main requirements of any audio watermarking systems to guarantee desired functionalities, but there is a tradeoff among them from the information-theoretic perspective. Generally, in order to enhance the imperceptibility, robustness, and payload simultaneously, the human auditory system and the statistical properties of the audio signal should be fully taken into account. The statistical model based transform domain multiplicative watermarking scheme embodies the above ideas, and therefore the detection and extraction of the multiplicative watermarks have received a great deal of attention. Although much effort has been made in recent years, improving the ability of imperceptibility, watermark capacity, and robustness at the same time remains a challenge within the audio watermarking community. In this paper, we propose a blind audio watermark decoder in stationary wavelet transform domain based on bivariate generalized Gaussian distributions, wherein both the local statistical properties and inter-scale dependencies of the stationary wavelet transform coefficients of digital audio are taken into account, and also the adaptive nonlinear watermark embedding strength functions are designed. The results of our tests with different host audios, digital watermarks, and various attacks, we experimentally confirm that the proposed approach performs well compared to the state-of-the-art audio watermarking methods.

Gram–Schmidt Orthogonalization-Based Audio Multiple Watermarking Scheme

This paper proposes an audio multi-watermarking scheme based on Gram–Schmidt orthogonalization. A random signal generator is applied to the host audio signal to generate the target segment for the local watermarking, thus improving the imperceptibility compared with the conventional global watermarking. The discrete cosine transform low-frequency coefficients of high stability are selected as watermark embedder, to ensure the robustness of this scheme. Consequently, the Gram–Schmidt orthogonalization process is employed to generate a set of orthogonal vectors, into which the spread transform dither modulation is applied to, respectively, embed the multiple watermark messages simultaneously. The orthogonality ensures that multiple watermark messages can be independently extracted without compromising the robustness against attacks. By embedding the multiple watermark messages into the same segment of the host audio signal, the watermark embedding capacity can be greatly enhanced while preserving the imperceptibility and robustness. A variety of experiments are conducted, and the results indicate the good performance of the proposed scheme. The proposed scheme has demonstrated superior performance gains over the state-of-the-art methods.

Wavelet packet transform-based medical image multiple watermarking with independent component analysis extraction

Rapid growth of internet in all aspects of life has led to the easy availability of the digital data to everyone. E-commerce, telemedicine, etc., are among the many applications of internet. Telemedicine is a crucial field where internet finds application. Healthcare professionals use internet to transmit and receive medical data. Thus the medical images can be shared, processed and transmitted through computer networks. All patient records, linked to the medical secrecy, must be confidential. Because of the importance of the security issues in the management of medical information, there is a need to develop watermarking techniques for protecting medical images. In this paper, colour medical image watermarking methods rely on wavelet packet transform (WPT) and extraction using independent component analysis (ICA). For watermark extraction, Pearson ICA is applied as it attains the new trait is that it not entail the renovation procedure in watermark extraction. The grades show that projected method is vigorous beside attacks such as Gaussian noise, salt and pepper noise, rotation and translation. The performance measures like PSNR, similarity measure and normalised correlation are assessed to confirm the robustness of the scheme.

Wavelet packet transform-based medical image multiple watermarking with independent component analysis extraction

Rapid growth of internet in all aspects of life has led to the easy availability of the digital data to everyone. E-commerce, telemedicine, etc., are among the many applications of internet. Telemedicine is a crucial field where internet finds application. Healthcare professionals use internet to transmit and receive medical data. Thus the medical images can be shared, processed and transmitted through computer networks. All patient records, linked to the medical secrecy, must be confidential. Because of the importance of the security issues in the management of medical information, there is a need to develop watermarking techniques for protecting medical images. In this paper, colour medical image watermarking methods rely on wavelet packet transform (WPT) and extraction using independent component analysis (ICA). For watermark extraction, Pearson ICA is applied as it attains the new trait is that it not entail the renovation procedure in watermark extraction. The grades show that projected method is vigorous beside attacks such as Gaussian noise, salt and pepper noise, rotation and translation. The performance measures like PSNR, similarity measure and normalised correlation are assessed to confirm the robustness of the scheme.

Blind Image Watermark Decoder in UDTCWT Domain Using Weibull Mixtures-Based Vector HMT

As one promising solution, digital watermarking has been proposed to resolve image copyright protection and content authentication, and has been applied successfully in many fields. Owing to their excellent description capability and invariance property, statistical models have become a popular tool for the image watermarking resulting in favorable trade-offs among imperceptibility, robustness and data payload. By modeling the robust undecimated dual tree complex Wavelet transform (UDTCWT) coefficient magnitudes with the Weibull mixtures based vector hidden Markov trees (HMT) and employing maximum likelihood (ML) test criterion, we propose a new image watermarking approach in UDTCWT domain in this paper. Our image watermarking approach consists of two parts, namely, embedding and extracting. In the embedding process, we compute the robust UDTCWT coefficient magnitudes with UDTCWT domain real/imaginary parts, and insert the digital watermark into the significant UDTCWT coefficient magnitude subband. In the extracting phase, robust UDTCWT coefficient magnitudes are firstly modeled by employing the Weibull mixture-based vector HMT, where the statistical properties of UDTCWT magnitudes are captured accurately. Then the expectation/conditional maximization (ECM) approach is introduced to estimate the statistical model parameters. Finally, an image watermark decoder for multiplicative watermarking is developed using the Weibull mixtures based vector HMT and ML test. The experiments show that the proposed method not only improves the imperceptibility, but also increases the robustness performance and outperforms state-of-the-art methods on a set of standard test images.

Dual Watermarking for Color Images: A New Image Copyright Protection Model based on the Fusion of Successive and Segmented Watermarking

Watermarking has been suggested as a generic technique to solve various problems associated with topics in the areas of digital rights management and multimedia security. Most of the early methods were based on single watermark embedding, but there are great limitations when single watermark embedding algorithms are tried into practical applications. The multiple watermarks are intended to convey different information at the same time. Current approaches rely on adding many watermarks in different bands or channels by means of scaling factor and embedding locations that are mainly defined by experts. This brought many challenges in achieving equilibrium between security, robustness, and quality. The aim of this work is to introduce an intelligent dual watermarking model for colour images that ensure image security for copyright protection (dual watermarking for single purpose). To optimize the dual watermarking requirements, the proposed model that employs both successive (re-) and segmented watermarking techniques is to be implemented with a multi-purpose evolutionary algorithm. Genetic algorithm is adopted to determine the embedding locations and scaling factors for different watermarks according to the features of each host image to balance between imperceptibility and robustness. The wavelet transform is utilized for salient features extraction because of its excellent space-frequency localization of salient image features. In addition, the suggested model encrypts the watermarks with the aid of Walsh transform; so that it is difficult to handle the watermarks even after being extracted by the attackers. Experimental results show that the proposed model is more robust against common image manipulation attacks in terms of PSNR and NCC.

Complex Wavelet-Based Image Watermarking with the Human Visual Saliency Model

Imperceptibility and robustness are the two complementary, but fundamental requirements of any digital image watermarking method. To improve the invisibility and robustness of multiplicative image watermarking, a complex wavelet based watermarking algorithm is proposed by using the human visual texture masking and visual saliency model. First, image blocks with high entropy are selected as the watermark embedding space to achieve imperceptibility. Then, an adaptive multiplicative watermark embedding strength factor is designed by utilizing texture masking and visual saliency to enhance robustness. Furthermore, the complex wavelet coefficients of the low frequency sub-band are modeled by a Gaussian distribution, and a watermark decoding method is proposed based on the maximum likelihood criterion. Finally, the effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack.

Multiple Watermarking for Images using Back-Propagation Neural Network and DWT

An effective multiple watermarking technique supported on neural network into the wavelet transform can be proposed. The wavelet coefficients has been preferred by Human Visual System. In the proposed work focus on Discrete Wavelet Transform based segmented image watermarking techniques using Back-Propagation neural networks. Using improved BPNN, the multiple watermarks are embedded into the original image, which can advance the pace of the learn, reduce the error and the qualified neural networks are extricate multiple watermarks as of the embedded images. The planned strategy achieves a excellent visual effect scheduled the watermarked images as well as high robustness on extracted multiple watermarks.

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Coefficient difference based watermark detector in nonsubsampled contourlet transform domain

Imperceptibility and robustness are two main requirements of any image watermarking systems to guarantee desired functionalities, but there is a tradeoff between them from the information–theoretic perspective. It is a challenging work to design a high performance digital watermarking scheme to keep a trade-off between imperceptibility and robustness. By modeling the nonsubsampled Contourlet transform (NSCT) coefficient differences with ranked set sample (RSS) based Cauchy distribution and employing locally most powerful (LMP) test, we propose a locally optimum image watermark detector in NSCT domain. In the proposed scheme, we first compute the robust coefficient differences according to the inter-scale dependency between NSCT coefficients, and then embed the digital watermark into the significant NSCT coefficient difference subband. At the watermark receiver, robust NSCT coefficient differences are firstly modeled by employing the Cauchy distribution, where the statistical properties of NSCT coefficient differences are captured accurately. Then the RSS approach is introduced to estimate the location parameter and shape parameter of Cauchy distribution. And finally an optimal detector for multiplicative watermarking is developed using the LMP decision rule and RSS-based Cauchy distribution. Also, we utilize the statistical model to derive the closed-form expressions for the watermark detector. Experimental results demonstrate the high efficiency of our watermarking scheme, which can provide better imperceptibility and outstanding robustness against various attacks, in comparison with the state-of-the-art approaches recently proposed in the literature.