Showing papers on "Digital watermarking published in 2021"
TL;DR: General concepts of watermarking, major characteristics, recent applications, concepts of embedding and recovery process of watermarks, and the summary of various techniques are highlighted in brief.
Abstract: With the widespread growth of medical images and improved communication and computer technologies in recent years, authenticity of the images has been a serious issue for E-health applications. In order to this, various notable watermarking techniques are developed by potential researchers. However, those techniques are unable to solve many issues that are necessary to be measured in future investigations. This paper surveys various watermarking techniques in medical domain. Along with the survey, general concepts of watermarking, major characteristics, recent applications, concepts of embedding and recovery process of watermark, and the summary of various techniques (in tabular form) are highlighted in brief. Further, major issues associated with medical image watermarking are also discussed to find out research directions for fledgling researchers and developers.
78 citations
TL;DR: Experimental results reveal that the proposed algorithm attains high robustness and improved security to the watermarked image against various kinds of attacks.
Abstract: Nowadays secure medical image watermarking had become a stringent task in telemedicine. This paper presents a novel medical image watermarking method by fuzzy based Region of Interest (ROI) selection and wavelet transformation approach to embed encrypted watermark. First, the source image will undergo fuzzification to determine the critical points through central and final intensity along the radial line for selecting region of interest (ROI). Second, watermark image is altered to time-frequency domain through wavelet decomposition where the sub-bands are swapped based on the magnitude value obtained through logistic mapping. In the each sub-band all the pixels get swapped, results in fully encrypted image which guarantees the watermark to a secure, reliable and an unbreakable form. In order to provide more robustness to watermark image, singular values are obtained for encrypted watermark image and key component is calculated for avoiding false positive error. Singular values of the source and watermark image are modified through key component. Experimental results reveal that the proposed algorithm attains high robustness and improved security to the watermarked image against various kinds of attacks.
73 citations
TL;DR: Two novel algorithms based on States of Matter Search (SMS) algorithm to find suitable embedding factors and reduce distortion are proposed for improved watermarking technology using meta-heuristic algorithm.
58 citations
TL;DR: The proposed scheme is found to be more useful, when compared with recently proposed schemes in term of features and usefulness.
Abstract: 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.
58 citations
TL;DR: The proposed blind dual watermarking scheme for color images is proposed by embedding an invisible robust watermark to protect copyright, as well as a fragile watermark is embedded for image authentication to provide a suitable mechanism to protect valuable and original color images.
Abstract: In this paper, a blind dual watermarking scheme for color images is proposed by embedding an invisible robust watermark to protect copyright, as well as a fragile watermark is embedded for image authentication. For the purpose of copyright protection, the robust watermark is embedded into the blue channel of RGB color space based on DWT, HVS and SVD domains using a specialized PSO optimization to balance the trade-off between robustness and imperceptibility. In addition, the robust watermarking capacity in SVD is doubled by inserting two robust watermark bits into each selected blocks and the robust watermark can be extracted blindly. For the purpose of authentication, a fragile watermark is embedded into all channels of RGB color space using a new way to manipulate the diagonal singular values. The authenticity of a suspected image can be verified in the absence of original watermark and host images. The combination of robust and fragile watermarking in the proposed scheme provides a suitable mechanism to protect valuable and original color images. According to the experimental and comparative results, the proposed scheme provides superior outcomes with high robustness, imperceptibility, and capacity along with a good accuracy rate in locating the tampered area of an image.
56 citations
TL;DR: Experimental results shows that the proposed methods maintain a high quality watermarked images and are very robust against several conventional attacks.
55 citations
TL;DR: Experimental and comparative results demonstrated the stability and improved performance of the proposed scheme compared to its parents watermarking schemes, and it is free of false positive detection error.
Abstract: This paper presents a new intelligent image watermarking scheme based on discrete wavelet transform (DWT) and singular values decomposition (SVD) using human visual system (HVS) and particle swarm optimization (PSO). The cover image is transformed by one-level (DWT) and subsequently the LL sub-band of (DWT) transformed image is chosen for embedding. To achieve the highest possible visual quality, the embedding regions are selected based on (HVS). After applying (SVD) on the selected regions, every two watermark bits are embedded indirectly into the U and $$V^{t}$$
components of SVD decomposition of the selected regions, instead of embedding one watermark bit into the U component and compensating on the $$V^{t}$$
component that results in twice capacity and reasonable imperceptibility. In addition, for increasing the robustness without losing the transparency, the scaling factors are chosen automatically by (PSO) based on the attacks test results and predefined conditions, instead of using fixed or manually set scaling factors for all different cover images. Experimental and comparative results demonstrated the stability and improved performance of the proposed scheme compared to its parents watermarking schemes. Moreover, the proposed scheme is free of false positive detection error.
55 citations
TL;DR: A new taxonomy of DNN watermarking is introduced and a few exemplarymethods belonging to each class are presented and hope that this paper will inspire new research in this exciting area and will help researchers to focus on the most innovative and challenging problems in the field.
54 citations
TL;DR: Li et al. as discussed by the authors proposed a new model watermarking framework for protecting deep networks trained for low-level computer vision or image processing tasks, which embeds a unified and invisible watermark into its outputs.
Abstract: Despite the tremendous success, deep neural networks are exposed to serious IP infringement risks. Given a target deep model, if the attacker knows its full information, it can be easily stolen by fine-tuning. Even if only its output is accessible, a surrogate model can be trained through student-teacher learning by generating many input-output training pairs. Therefore, deep model IP protection is important and necessary. However, it is still seriously under-researched. In this work, we propose a new model watermarking framework for protecting deep networks trained for low-level computer vision or image processing tasks. Specifically, a special task-agnostic barrier is added after the target model, which embeds a unified and invisible watermark into its outputs. When the attacker trains one surrogate model by using the input-output pairs of the barrier target model, the hidden watermark will be learned and extracted afterwards. To enable watermarks from binary bits to high-resolution images, a deep invisible watermarking mechanism is designed. By jointly training the target model and watermark embedding, the extra barrier can even be absorbed into the target model. Through extensive experiments, we demonstrate the robustness of the proposed framework, which can resist attacks with different network structures and objective functions.
53 citations
TL;DR: The experimental results prove that the DWFCAT is highly efficient compared with the various state-of-the-art approaches for authentication and tamper localization of industrial images and can withstand a range of hybrid signal processing and geometric attacks.
Abstract: The image data received through various sensors are of significant importance in Industry 4.0. Unfortunately, these data are highly vulnerable to various malicious attacks during its transit to the destination. Although the use of pervasive edge computing (PEC) with the Internet of Things (IoT) has solved various issues, such as latency, proximity, and real-time processing, but the security and authentication of data between the nodes is still a significant concern in PEC-based industrial-IoT scenarios. In this article, we present “DWFCAT,” a dual watermarking framework for content authentication and tamper localization for industrial images. The robust and fragile watermarks along with overhead bits related to the cover image for tamper localization are embedded in different planes of the cover image. We have used discrete cosine transform coefficients and exploited their energy compaction property for robust watermark embedding. We make use of a four-point neighborhood to predict the value of a predefined pixel and use it for embedding the fragile watermark bits in the spatial domain. Chaotic and deoxyribonucleic acid encryption is used to encrypt the robust watermark before embedding to enhance its security. The results indicate that DWFCAT can withstand a range of hybrid signal processing and geometric attacks, such as Gaussian noise, salt and pepper, joint photographic experts group (JPEG) compression, rotation, low-pass filtering, resizing, cropping, sharpening, and histogram equalization. The experimental results prove that the DWFCAT is highly efficient compared with the various state-of-the-art approaches for authentication and tamper localization of industrial images.
50 citations
TL;DR: A robust double-encrypted watermarking algorithm based on the fractional Fourier transform and discrete cosine transform in invariant wavelet domain is proposed, which exhibits high robustness under the premise of satisfying security, reliability and invisibility.
01 Jul 2021
TL;DR: This paper shows how to watermark cryptographic functions such as PRFs using indistinguishability obfuscation and builds a watermarking scheme from standard assumptions that achieve this strong mark-unremovability property.
Abstract: A software watermarking scheme allows one to embed a “mark” into a program without significantly altering the behavior of the program. Moreover, it should be difficult to remove the watermark without destroying the functionality of the program. Recently, Cohen et al. (STOC 2016) and Boneh et al. (PKC 2017) showed how to watermark cryptographic functions such as PRFs using indistinguishability obfuscation. Notably, in their constructions, the watermark remains intact even against arbitrary removal strategies. A natural question is whether we can build watermarking schemes from standard assumptions that achieve this strong mark-unremovability property.
TL;DR: In this paper, the authors proposed a digital watermarking framework suitable for deep neural networks that output images as the results, in which any image outputted from a watermarked neural network must contain a certain watermark.
Abstract: Watermarking neural networks is a quite important means to protect the intellectual property (IP) of neural networks. In this paper, we introduce a novel digital watermarking framework suitable for deep neural networks that output images as the results, in which any image outputted from a watermarked neural network must contain a certain watermark. Here, the host neural network to be protected and a watermark-extraction network are trained together, so that, by optimizing a combined loss function, the trained neural network can accomplish the original task while embedding a watermark into the outputted images. This work is totally different from previous schemes carrying a watermark by network weights or classification labels of the trigger set. By detecting watermarks in the outputted images, this technique can be adopted to identify the ownership of the host network and find whether an image is generated from a certain neural network or not. We demonstrate that this technique is effective and robust on a variety of image processing tasks, including image colorization, super-resolution, image editing, semantic segmentation and so on.
TL;DR: A blind and robust watermarking technique that allows the integration of the electronic patient’s record into a computerized tomography scan and offers excellent imperceptibility and very good robustness against several geometric and destructive attacks is presented.
Abstract: In order to contribute to the security of medical image, we present in this paper a blind and robust watermarking technique that allows the integration of the electronic patient’s record into a computerized tomography scan. In this approach, a discrete wavelet transform is applied to the image before the integration process, then, a topological reorganization of the coefficients of the LL sub-bands is done by the ZigZag scanning method. The obtained coefficients are then combined to integrate the watermark bits. A hash of the electronic patient record being integrated in the image, the integrity of the watermark can easily be verified. After the evaluation of our approach in terms of invisibility and robustness, the experimental results obtained show that our approach offers excellent imperceptibility (with a PSNR above 70 dB) and very good robustness against several geometric and destructive attacks.
TL;DR: In this article, the authors proposed a blended detection scheme that combines watermarking and moving target to detect advanced stealthy cyber-attacks against networked control systems, including zero-dynamics, replay, and covert attacks.
Abstract: In recent years, different solutions have been proposed to detect advanced stealthy cyber-attacks against networked control systems. In this article, we propose a blended detection scheme that properly leverages and combines two existing detection ideas, namely, watermarking and moving target . In particular, a watermarked signal and a nonlinear static auxiliary function are combined to both limit the attacker's disclosure resources and obtain an unidentifiable moving target. The proposed scheme is capable of detecting a broad class of false data injection attacks, including zero-dynamics, replay, and covert attacks. Moreover, it is shown that the proposed approach mitigates the drawbacks of standard moving target and watermarking defense strategies. Finally, an extensive simulation study is reported to contrast the proposed detector with recent competitor schemes and provide tangible evidence of the effectiveness of the proposed solution.
01 Feb 2021
TL;DR: Robustness and imperceptibility of the proposed technique is enhanced as depicted in experimental results under various attacks, and better robustness is attained from proposed technique on comparing it with other formerly reported schemes.
Abstract: In this paper, a novel medical image watermarking (MIW) technique for tele ‐ medicine applications is proposed. In this approach homomorphic transform (HT), redundant discrete wavelet tran...
TL;DR: In this article, a blind watermarking approach for medical image protection is proposed, which consists of the Electronic Patient Record and the image acquisition data, and the watermark is then integrated into the least significant bits of the S component obtained by combining the parity of the successive coefficients.
TL;DR: Two new substitution schemes for digital audio watermarking based on the Fourier transform are proposed and show that this approach offers good imperceptibility and generates watermarked audio sample robust against various attacks with a high-quality watermark.
TL;DR: Wang et al. as discussed by the authors proposed a robust and blind image watermarking scheme based on deep learning neural networks, which is trained in an unsupervised manner to avoid human intervention and annotation.
Abstract: Digital image watermarking is the process of embedding and extracting a watermark covertly on a cover-image. To dynamically adapt image watermarking algorithms, deep learning–based image watermarking schemes have attracted increased attention during recent years. However, existing deep learning–based watermarking methods neither fully apply the fitting ability to learn and automate the embedding and extracting algorithms, nor achieve the properties of robustness and blindness simultaneously. In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed. To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm. A deep learning architecture is specially designed for image watermarking tasks, which will be trained in an unsupervised manner to avoid human intervention and annotation. To facilitate flexible applications, the robustness of the proposed scheme is achieved without requiring any prior knowledge or adversarial examples of possible attacks. A challenging case of watermark extraction from phone camera–captured images demonstrates the robustness and practicality of the proposal. The experiments, evaluation, and application cases confirm the superiority of the proposed scheme.
TL;DR: A blind and robust scheme using YCbCr color space, IWT (integer wavelet transform) and DCT (discrete cosine transform) for color image watermarking and the ANN framework provides faster embedding with approximately similar parametric results.
TL;DR: In this article, the state-of-the-art in hybrid SVD-based image watermarking is analyzed and compared to highlight various security problems, open issues, and research gaps.
Abstract: Watermarking is an important technique for protecting sensitive e-multimedia data and intellectual property. Watermarking techniques are used for many applications such as ownership protection, which is a popular area of research as compared to others such as authentication and local temper localization. There are many image watermarking schemes that have been recently published based on the frequency domain as it can fulfill watermarking requirements such as high robustness and imperceptibility. Singular-value decomposition (SVD) is one of them and is used in many frequency transform-based image watermarking schemes due to its stability and mathematical simplicity. However, there are many schemes that lack robustness against malicious cyberattacks, whereby the watermarks are easy to detect and destroy. Consequently, the proposed watermarking schemes became more complicated and cannot resist various geometric and non-geometric attacks. Thus, there are many existing hybrid SVD-based image watermarking schemes found be insecure. As there is also a lack of in-depth reviews in this domain, the focus of this paper is the analysis of the state-of-the-art in hybrid SVD-based image watermarking. We perform efficiency comparisons to highlight various security problems, open issues, and research gaps. Based on our findings, we additionally provide some recommendations for the development of more robust schemes in the future. This paper provides essential information for researchers and practitioners alike to advance the field of image watermarking.
TL;DR: The experimental results demonstrate that the proposed adaptive multiple embedding factors (AMEF) algorithm can effectively achieve the trade-off among the invisibility, robustness and capacity, simultaneously.
Abstract: 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.
TL;DR: Soft computing based watermarking approaches providing robustness, imperceptibility and good embedding capacity are compared systematically and major issues and potential solutions for soft computing-basedWatermarking are discussed to encourage further research in this area.
Abstract: Image watermarking techniques are used to provide copyright protection and verify ownership of media/entities. This technique refers to the concept of embedding of secret data/information of an owner in a given media/entity for determining any ownership conflicts that can arise. Many watermarking approaches have been offered by various authors in the last few years. However, there are not enough studies and comparisons of watermarking techniques in soft computing environments. Nowadays, soft computing techniques are used to improve the performance of watermarking algorithms. This paper surveys soft computing-based image watermarking for several applications. We first elaborate on novel applications, watermark characteristics and different kinds of watermarking systems. Then, soft computing based watermarking approaches providing robustness, imperceptibility and good embedding capacity are compared systematically. Furthermore, major issues and potential solutions for soft computing-based watermarking are also discussed to encourage further research in this area. Thus, this survey paper will be helpful for researchers to implement an optimized watermarking scheme for several applications.
TL;DR: This work proposes a neural network "laundering" algorithm to remove black-box backdoor watermarks from neural networks even when the adversary has no prior knowledge of the structure of the watermark.
TL;DR: The experimental results show that the proposed fusion-domain color watermarking based on Haar transform and image correction is feasible and has good performance.
Abstract: The leapfrog development of computer technology has greatly enhanced the breadth of information dissemination. As a larger information carrier, color image becomes more and more popular, but the copyright protection problem becomes more and more serious. To solve this problem, this paper proposes a fusion-domain color watermarking based on Haar transform and image correction. Firstly, the maximum energy coefficient of Haar transform is directly obtained in spatial domain. Then, the coefficient is quantified with the help of variable quantization steps to embed the color watermark that encrypted by affine transform. If the watermarked image is processed by geometric attack, then the attacked image can be corrected by using of the geometric properties. Finally, the inverse embedding process is performed to extract the watermark. The performances of the proposed method are shown as follows: 1) all PSNR (Peak Signal-to-Noise Ratio) values are greater than 40 dB; 2) all SSIM (Structural Similarity Index Metric) values are greater than 0.96; 3) most NC (Normalized Cross-correlation) values are more than 0.9; 4) the key space is more than 2432; 5) the maximum embedded capacity is 0.25bpp; 6) the running time is about 6 s. Compared with the related methods, the experimental results show that the proposed method is feasible and has good performance.
TL;DR: Experimental results show that the proposed robust watermarking method can effectively reduce the compensation information, and the new cover-lossless robustWatermarking system provides strong robustness to those content-preserving manipulations including scaling, rotation, JPEG compression and other noise-like manipulations.
Abstract: Cover-lossless robust watermarking is a new research issue in the information hiding community, which can restore the cover image completely in case of no attacks. Most countermeasures proposed in the literature usually focus on additive noise-like manipulations such as JPEG compression, low-pass filtering and Gaussian additive noise, but few are resistant to challenging geometric deformations such as rotation and scaling. The main reason is that in the existing cover-lossless robust watermarking algorithms, those exploited robust features are related to the pixel position. In this article, we present a new cover-lossless robust image watermarking method by efficiently embedding a watermark into low-order Zernike moments and reversibly hiding the distortion due to the robust watermark as the compensation information for restoration of the cover image. The amplitude of the exploited low-order Zernike moments are: 1) mathematically invariant to scaling the size of an image and rotation with any angle; and 2) robust to interpolation errors during geometric transformations, and those common image processing operations. To reduce the compensation information, the robust watermarking process is elaborately and luminously designed by using the quantized error, the watermarked error and the rounded error to represent the difference between the original and the robust watermarked image. As a result, a cover-lossless robust watermarking system against geometric deformations is achieved with good performance. Experimental results show that the proposed robust watermarking method can effectively reduce the compensation information, and the new cover-lossless robust watermarking system provides strong robustness to those content-preserving manipulations including scaling, rotation, JPEG compression and other noise-like manipulations. In case of no attacks, the cover image can be recovered without any loss.
TL;DR: The comparison of simulation results with other similar algorithms shows that the proposed method performs better than any of these methods in terms of visual quality analysis and attack resistance and can be used as an efficient robust algorithm in applied processes.
Abstract: In this paper, using a new two-dimensional complex map, a secure video watermarking system is presented. Standard analyzes have been performed to analyze a dynamical system to prove the existence of chaos in the proposed map and the results indicate a chaotic behavior in this complex chaotic map. In addition, an efficient algorithm based on IWT, DWT, and CT transforms with the participation of single value decomposition for the embedding and extraction process is introduced. The simulation results showed that the proposed algorithm has good visual quality based on criteria such as PSNR and SSIM. Geometric and non-geometric attacks were also performed on the video obtained by watermarking, and the results showed that the proposed algorithm in many attacks with a value of 1.00 for the NC criterion can be a very robust algorithm against attacks. A correlation-based process for detecting the rotational attack is also presented which makes the rotational geometric attack successfully pass. The comparison of simulation results with other similar algorithms shows that the proposed method performs better than any of these methods in terms of visual quality analysis and attack resistance and can be used as an efficient robust algorithm in applied processes.
TL;DR: A Robust Reversible Watermarking scheme in Encrypted Image with Secure Multi-party (RRWEI-SM) based on lightweight cryptography is first proposed and is demonstrated to be secure, robust and effective.
Abstract: With the rapid development of network media, increasing research on reversible watermarking has focused on improving its robustness to resisting attacks during digital media transmission. There are some other reversible watermarking schemes that work in the encrypted domain for preserving the privacy of the cover image. The robustness of the watermarking and the privacy preserving of the cover image have become the key factors of reversible watermarking. However, there are few robust reversible watermarking schemes in the encrypted domain that could resist common attacks (such as JPEG compression, noise addition) and preserve privacy at the same time. In addition, the embedding capacity of a robust watermark and the efficiency of the encryption method must be considered. Recently, cloud computing technology has led to the rapid growth of network media, and many multimedia properties are owned by multiple parties, such as a film’s producer and multiple distributors. Multi-party watermarking has become an important demand for network media to protect all parties’ rights. In this paper, a Robust Reversible Watermarking scheme in Encrypted Image with Secure Multi-party (RRWEI-SM) based on lightweight cryptography is first proposed. Additive secret sharing and block-level scrambling are developed to generate the encrypted image. Then, the robust reversible watermarking based on significant bit Prediction Error Expansion (PEE) is performed by Secure Multi-party Computation (SMC). For applications with high robustness, a Modified RRWEI-SM is proposed by exploiting a two-stage architecture. Furthermore, both the RRWEI-SM scheme and Modified RRWEI-SM scheme are separable and can be applied to multiparty copyright protection. The experimental results and theoretical analysis demonstrate here that the RRWEI-SM and the Modified RRWEI-SM are secure, robust and effective.
TL;DR: A black-box watermarking method for pretrained models, which exploits the overparameterization of the DNNs in image processing, with a particular focus on low-level image processing tasks that map images to images.
Abstract: Publishing/sharing pretrained deep neural network (DNN) models is a common practice in the community of computer vision. The increasing popularity of pretrained models has made it a serious concern: how to protect the intellectual properties of model owners and avert illegal usages by malicious attackers. This article aims at developing a framework for watermarking DNNs, with a particular focus on low-level image processing tasks that map images to images. Using image denoising and superresolution as case studies, we develop a black-box watermarking method for pretrained models, which exploits the overparameterization of the DNNs in image processing. In addition, an auxiliary module for visualizing the watermark information is proposed for further verification. Extensive experiments show that the proposed watermarking framework has no noticeable impact on model performance and enjoys the robustness against the often-seen attacks.
TL;DR: The proposed blind and robust approach for medical image protection consists in embedding patient information and image acquisition data in the image and the integration was performed in the medium frequencies of the image.
Abstract: In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks.