Watermarking Framework Research Articles

Finding robust domain from attacks: A learning framework for blind watermarking

In recent years, some researchers have been interested in whether robustness and blindness can be simultaneously secured in a watermarking based on machine learning. However, achieving robustness against various attacks at once is still difficult for watermarking techniques. To address the problem, in this paper, we propose a learning framework for robust and blind watermarking based on reinforcement learning. We repeat three stages: watermark embedding, attack simulation, and weight updating. Specifically, we present image watermarking networks called WMNet using convolutional neural networks (CNNs). Two methods to embed a watermark are proposed and these two methods are based on backpropagation and autoencoder, respectively. We can optimize the robustness while carefully considering the invisibility of the watermarking system. The experimental results show that the trained WMNet captures more robust features than the current watermarking schemes, which use the frequency domain. The trade-off between the robustness and the invisibility of each technique was measured. Also, we adopt a visual masking with which we can achieve the appropriate balance between robustness and invisibility of the watermark. Our reinforcement-learning-based technique has better robustness than the existing techniques for both attacks seen in learning and unseen attacks. Due to the generalization ability of WMNet, moreover, it shows high robustness against multiple attacks and various levels of attacks which are not considered in training stage.

Orientation-Aware Saliency Guided JND Model for Robust Image Watermarking

The visual saliency-based just noticeable distortion (VS-JND) model has been widely used in the quantization-based watermarking framework, but it remains a challenge that how to reflect the characteristics of the human visual system (HVS) to the greatest extent in the processing of detecting salient objects. In this paper, given that the HVS has a different perception sensitivity in the different orientation of the image, a compound orientation feature map, which contains the vertical, horizontal, and diagonal information for directional patch extraction, is computed to improve the visual saliency map within just noticeable distortion (JND) profile. First, the DC and three low-frequency coefficients of each block are used to calculate the luminance and texture feature map, respectively. Then, the feature map based on the complex directional feature includes the horizontal, vertical, and diagonal information contained in the image is calculated by three low-frequency coefficients. Finally, we will be able to make a linear fusion of the same three different visual features to get the final VS map, and the JND model is improved according to the new VS model. Later, the new proposed VS-JND model is employed in the quantization watermarking (Q-watermarking) framework. The simulation results show that the proposed watermarking scheme is effective and produces superior robustness compared with existing monochrome image watermarking schemes.

Robust Image Watermarking Based on Two-Layer Visual Saliency-Induced JND Profile

Visual saliency (VS) is an important mechanism for defining which areas of an image will attract more attention of the HVS. Thus, VS can be employed to weight the just noticeable difference (JND) with different attention levels. Some VS-based JND profiles have been proposed in the DCT domain, which used the bottom-up features, such as luminance and texture only. Recently, the research about saliency detection has shown that a better saliency model considering both bottom-up features and top-down features will lead to a significant improvement for the overall saliency detection performance. In this paper, we propose a novel two-layer VS-induced JND profile, which is composed of the bottom-up features and the top-down feature extracted from DCT blocks in the transformed domain. In this model, the luminance and texture features are adapted to calculate the bottom-up features maps, while the top-down feature of focus is used to guide the generation of final salient regions since the camerapersons are used to facilitate the attention regions in focus. The proposed two-layer saliency-induced JND model is further applied to modulate the quantization step in the watermarking framework, which can make full use of its individual merits to achieve a better tradeoff between fidelity and robustness. The experimental results show that the proposed scheme has superior performance than the previous watermarking schemes.

An Imperceptible Medical Image Watermarking Framework for Automated Diagnosis of Retinal Pathologies in an eHealth Arrangement

The newly driven concept of electronic health (eHealth) has made distance irrelevant in healthcare. eHealth, which is commonly known as an electronic form of healthcare, refers broadly to the use of advanced information and communication technology within healthcare environments. However, its practical implementation poses many sets of challenges. One of the major challenges is authentication and integrity verification of vital medical data. Proper verification of patients with associated medical images or data is crucial to conduct accurate medical diagnostics. This paper presents an imperceptible watermarking-based security framework to address the issues of authentication and integrity verification of medical images for eHealth applications. The electronic patient record (EPR), as a watermark image, is embedded in optical coherence tomography (OCT)/fundus scan (consisting of healthy and diseased scans), using a hybrid watermarking technique based on fast curvelet transform (FCT) and singular value decomposition (SVD). The proposed work showed a high level of robustness, imperceptibility, and security for medical images when compared with the state-of-the-art existing watermarking techniques. In addition, we conducted a comparative analysis of the watermarked OCT/fundus scans with non-watermarked scans, and our results validate that the insertion of the watermark in the retinal images does not affect the automated medical image diagnosis of various retinal pathologies. Moreover, the correct recovery of the EPR from the watermarked scans makes the proposed framework applicable to the authentication of medical images for a computer-based automated diagnostic system in an eHealth arrangement.

Dual watermarking framework for privacy protection and content authentication of multimedia

In the current multimedia networked infrastructure, privacy breaches due to cyber-attacks result in huge economic losses. Despite these threats there is ever increasing demand to share data over various insecure networks for accomplishment of numerous tasks. In such a scenario there is a greater need to develop new algorithms for strengthening the existing cybersecurity frameworks, ensure security, privacy, copyright protection and authentication of data. In this paper a new technique for copyright protection, data security and content authentication of multimedia images is presented. The copyright protection of the media is taken care of by embedding a robust watermark using an efficient inter-block coefficient differencing algorithm and is proposed as Scheme I. Scheme II has been utilized to ensure both copyright protection, and content authentication. The authentication of the content has been ensured by embedding a fragile watermark in spatial domain while as copyright protection has been taken care of utilizing a robust watermark. In order to thwart an adversary and ensure that it has no access to actual embedded data, we make use of a novel encryption algorithm in conjunction with Arnold transform to encrypt data prior to its embedding. The experimental results reveal that the proposed framework offers high degree of robustness against single/dual/triple attacks; with Normalized Correlation (NCC), more than or equal to 0.95. Besides, the fragile watermark embedding makes the system capable of tamper detection and localization with average BER more than 45% for all signal processing/geometric attacks. The average PSNR achieved for both schemes is greater than 41 dB. A comparison of the proposed framework with various state-of-the-art techniques demonstrate its effectiveness and superiority.

2D Vector Map Fragile Watermarking with Region Location

Locating the original region of tampered features is a challenging task for existing 2D vector map fragile watermarking methods. This article presents a 2D vector map fragile watermarking framework that locates not only the current but also the original region of tampered feature groups. In particular, we propose dividing the features of the host vector map into groups, and embedding a watermark consisting of location-bits and check-bits into each group at the sender side. At the receiver side, by comparing the extracted and calculated check-bits, one can identify tampered groups and locate their current regions. Then the location-bits extracted from the mapping groups are used to indicate the original regions of the tampered groups. To demonstrate and analyze the applicability of this framework, we instantiate it by proposing a simulated annealing (SA)-based group division method, a group mapping method, a minimum encasing rectangle (MER) based location-bits generation method and a check-bits generation method, and use an existing reversible data hiding method for watermark embedding. The experimental results show that the proposed framework can locate all the regions influenced by tampering, and the SA-based group division method can get a better region location ability.

Random Matching Pursuit for Image Watermarking

The classical solution to an underdetermined system of linear equations mainly has two opposite directions, which lead to either a large $\ell _{2}$ -norm sparse solution or a non-sparse minimum $\ell _{2}$ -norm solution. In this paper, we systematically show that by modifying the well-known basic matching pursuit algorithm originally proposed to identify the sparse solution, an alternative solution between the two classical ones could be obtained. The modified algorithm, termed as random matching pursuit (RMP), is then used to create a novel image watermarking framework. Compared to conventional systems, the security is substantially improved by the use of random over-complete dictionaries and the order parameter of RMP. Capacity can also be increased thanks to the transform with over-complete dictionaries that could expand signal dimension. Meanwhile, imperceptibility and robustness properties of the proposed design framework are not compromised. The classical spread spectrum and improved spread spectrum techniques are applied to the proposed framework for practical implementations. The novelty and effectiveness of the proposed systems are supported by rigorous performance analysis and experimental results using an image data set. This paper reveals the potential of using over-complete dictionaries in multimedia watermarking systems, which theoretically leads to the exploration of alternative candidates among the infinite solutions to underdetermined linear systems other than minimum $\ell _{2}$ -norm and sparse ones.

Hierarchical watermarking framework based on analysis of local complexity variations

Increasing production and exchange of multimedia content have increased the need for better protection of copyright using watermarking. Different methods have been proposed to satisfy the tradeoff between imperceptibility and robustness as two important characteristics in watermarking while maintaining proper data-embedding capacity. Many watermarking methods use independent image set of parameters. Different images possess different potentials for the robust and transparent hosting of watermark data. To overcome this deficiency, in this paper we have proposed a new hierarchical adaptive watermarking framework. At the higher level of the hierarchy, the complexity of an image is ranked in comparison with complexities of images of a dataset. For a typical dataset of images, the statistical distribution of block complexities is found. At the lower level of the hierarchy, for a single cover image that is to be watermarked, complexities of blocks can be found. Local complexity variation among a block and its neighbors is used to change the watermark strength factor of each block adaptively. Such local complexity analysis creates an adaptive embedding scheme, which results in higher transparency by reducing blockiness effects. This two-level hierarchy has enabled our method to take advantage of all image blocks to elevate the embedding capacity while preserving imperceptibility. For testing the effectiveness of the proposed framework, contourlet transform in conjunction with discrete cosine transform is used to embed pseudorandom binary sequences as a watermark. Experimental results show that the proposed framework elevates the performance the watermarking routine regarding both robustness and transparency.

Semantics-based software watermarking by abstract interpretation

Software watermarking is a software protection technique used to defend the intellectual property of proprietary code. In particular, software watermarking aims at preventing software piracy by embedding a signature, i.e. an identifier reliably representing the owner, in the code. When an illegal copy is made, the owner can claim his/her identity by extracting the signature. It is important to hide the signature in the program in order to make it difficult for the attacker to detect, tamper or remove it. In this work, we present a formal framework for software watermarking, based on program semantics and abstract interpretation, where attackers are modelled as abstract interpreters. In this setting, we can prove that the ability to identify signatures can be modelled as a completeness property of the attackers in the abstract interpretation framework. Indeed, hiding a signature in the code corresponds to embed it as a semantic property that can be retrieved only by attackers that are complete for it. Any abstract interpreter that is not complete for the property specifying the signature cannot detect, tamper or remove it. We formalize in the proposed framework the major quality features of a software watermarking technique: secrecy, resilience, transparence and accuracy. This provides a unifying framework for interpreting both watermarking schemes and attacks, and it allows us to formally compare the quality of different watermarking techniques. Indeed, a large number of watermarking techniques exist in the literature and they are typically evaluated with respect to their secrecy, resilience, transparence and accuracy to attacks. Formally identifying the attacks for which a watermarking scheme is secret, resilient, transparent or accurate can be a complex and error-prone task, since attacks and watermarking schemes are typically defined in different settings and using different languages (e.g. program transformation vs. program analysis), complicating the task of comparing one against the others.

Evaluvation of Multiresolution Watermarking Algorithm

Now-a-days watermarking plays a pivotal role in most of the industries for providing security to their own as well as hired or leased data. This paper its main aim is to study the multiresolution watermarking algorithms and also choosing the effective and efficient one for improving the resistance in data compression. Computational savings from such a multiresolution watermarking framework is obvious. The multiresolutional property makes our watermarking scheme robust to image/video down sampling operation by a power of two in either space or time. There is no common framework for multiresolutional digital watermarking of both images and video. A multiresolution watermarking based on the wavelet transformation is selected in each frequency band of the Discrete Wavelet Transform (DWT) domain and therefore it can resist the destruction of image processing. The rapid development of Internet introduces a new set of challenging problems regarding security. One of the most significant problems is to prevent unauthorized copying of digital production from distribution. Digital watermarking has provided a powerful way to claim intellectual protection. We proposed an idea for enhancing the robustness of extracted watermarks. Watermark can be treated as a transmitted signal, while the destruction from attackers is regarded as a noisy distortion in channel. For the implementation, we have used minimum nine coordinate positions. The watermarking algorithms to be taken for this study are Corvi algorithm and Wang algorithm. In all graph, we have plotted X axis as peak signal to noise ratio (PSNR) and y axis as Correlation with original watermark. The threshold value ά is set to 5. The result is smaller than the threshold value then it is feasible, otherwise it is not.

A new image watermarking framework based on levels-directions decomposition in contourlet representation

With the development of digital information technologies, robust watermarking framework is taken into real consideration as a challenging issue in the area of image processing, due to the large applicabilities and its utilities in a number of academic and real environments. There are a wide range of solutions to provide image watermarking frameworks, while each one of them is attempted to address an efficient and applicable idea. In reality, the traditional techniques do not have sufficient merit to realize an accurate application. Due to the fact that the main idea behind the approach is organized based on contourlet representation, the only state-of-the-art materials that are investigated along with an integration of the aforementioned contourlet representation in line with watermarking framework are concentrated to be able to propose the novel and skilled technique. In a word, the main process of the proposed robust watermarking framework is organized to deal with both new embedding and de-embedding processes in the area of contourlet transform to generate watermarked image and the corresponding extracted logo image with high accuracy. In fact, the motivation of the approach is that the suggested complexity can be of novelty, which consists of the contourlet representation, the embedding and the corresponding de-embedding modules and the performance monitoring including an analysis of the watermarked image as well as the extracted logo image. There is also a scrambling module that is working in association with levels-directions decomposition in contourlet embedding mechanism, while a decision maker system is designed to deal with the appropriate number of sub-bands to be embedded in the presence of a series of simulated attacks. The required performance is tangibly considered through an integration of the peak signal-to-noise ratio and the structural similarity indices that are related to watermarked image. And the bit error rate and the normal correlation are considered that are related to the extracted logo analysis, as well. Subsequently, the outcomes are fully analyzed to be competitive with respect to the potential techniques in the image colour models including hue or tint in terms of their shade, saturation or amount of gray and their brightness via value or luminance and also hue, saturation and intensity representations, as long as the performance of the whole of channels are concentrated to be presented. The performance monitoring outcomes indicate that the proposed framework is of significance to be verified.

An Efficient Framework for Compressed Domain Watermarking in P Frames of High-Efficiency Video Coding (HEVC)--Encoded Video

Digital watermarking has received much attention in recent years as a promising solution to copyright protection. Video watermarking in compressed domain has gained importance since videos are stored and transmitted in a compressed format. This decreases the overhead to fully decode and re-encode the video for embedding and extraction of the watermark. High Efficiency Video Coding (HEVC/H.265) is the latest and most efficient video compression standard and a successor to H.264 Advanced Video Coding. In this article, we propose a robust watermarking framework for HEVC-encoded video using informed detector. A readable watermark is embedded invisibly in P frames for better perceptual quality. Our framework imposes security and robustness by selecting appropriate blocks using a random key and the spatio-temporal characteristics of the compressed video. A detail analysis of the strengths of different compressed domain features is performed for implementing the watermarking framework. We experimentally demonstrate the utility of the proposed work. The results show that the proposed work effectively limits the increase in video bitrate and degradation in perceptual quality. The proposed framework is robust against re-encoding and image processing attacks.

General Framework of Reversible Watermarking Based on Asymmetric Histogram Shifting of Prediction Error

This paper presents a general framework for the reversible watermarking based on asymmetric histogram shifting of prediction error, which is inspired by reversible watermarking of prediction error. Different from the conventional algorithms using single-prediction scheme to create symmetric histogram, the proposed method employs a multi-prediction scheme, which calculates multiple prediction values for the pixels. Then, the suitable value would be selected by two dual asymmetric selection functions to construct two asymmetric error histograms. Finally, the watermark is embedded in the two error histograms separately utilizing a complementary embedding strategy. The proposed framework provides a new perspective for the research of reversible watermarking, which brings about many benefits for the information security.

An Efficient Watermarking Technique for Biometric Images

This paper provides a good way of digital watermarking framework using biometric image as watermark by the application of homomorphic encryption system. Thus, it provides data confidentiality and also authenticity. Here, the biometric image is watermarked inside the standard benchmark images. The robustness of the watermarks in the image are verified through measurement of Peak Signal-to-noise ratio (PSNR), Correlation coefficient (CC%), and noise test. The biometric images are obtained by subtracting watermarked images with benchmark images.

Towards a robust and fully reversible image watermarking framework based on number theoretic transform

This paper presents a new watermarking approach in the spectral domain based on the number theoretic transform (NTT) to ensure sharing and transferring medical images in a secure way. Through this approach we ensure also the robustness of the embedded watermarks within the medical image to prove ownership. The NTT's effectiveness has been proven in the images lossless transmission and in convolution fast calculation. In this new approach, the watermark is embedded into the NTT image based on linear interpolation with a specific factor controlling the visibility/invisibility of the watermark. The extraction process will be performed on the attacked watermarked image in order to extract the attacked watermark. We are particularly interested in the NTT since it is considered as a fully reversible transform without any loss when choosing the suitable parameters. We measured the robustness of the proposed approach by the commonly used metrics against several scenarios of attacks (geometric and non-geometric) such as JPEG compression, adding noise, rotation, median filtering, etc. The tests are performed on several types of medical images. The obtained results are very encouraging and represent a remarkable robustness which will be detailed in this paper.

Towards a robust and fully reversible image watermarking framework based on number theoretic transform

Towards a robust and fully reversible image watermarking framework based on number theoretic transform

Parameterization of LSB in Self-Recovery Speech Watermarking Framework in Big Data Mining

The privacy is a major concern in big data mining approach. In this paper, we propose a novel self-recovery speech watermarking framework with consideration of trustable communication in big data mining. In the framework, the watermark is the compressed version of the original speech. The watermark is embedded into the least significant bit (LSB) layers. At the receiver end, the watermark is used to detect the tampered area and recover the tampered speech. To fit the complexity of the scenes in big data infrastructures, the LSB is treated as a parameter. This work discusses the relationship between LSB and other parameters in terms of explicit mathematical formulations. Once the LSB layer has been chosen, the best choices of other parameters are then deduced using the exclusive method. Additionally, we observed that six LSB layers are the limit for watermark embedding when the total bit layers equaled sixteen. Experimental results indicated that when the LSB layers changed from six to three, the imperceptibility of watermark increased, while the quality of the recovered signal decreased accordingly. This result was a trade-off and different LSB layers should be chosen according to different application conditions in big data infrastructures.

Framework for robust blind image watermarking based on classification of attacks

With the widespread internet usage, digital contents are easily distributed throughout the world. To eliminate concerns of producers and owners of digital contents, watermarking techniques are extensively being used. Robustness against intentional and unintentional attacks is a major quality of watermarking systems. Since different attacks tend to target different parts of the frequency spectrum, in this paper we propose a framework for blind watermarking which determines the type of attack that the image has gone through before extracting the watermark. Within this framework, we propose an attack classification method to identify the region of the frequency spectrum that is less damaged. The watermark which is redundantly spread throughout the spectrum can be extracted from the less damaged regions. Experimental results show functionality of the framework by producing better results in comparison with well-known blind watermarking techniques.

Level-direction decomposition analysis with a focus on image watermarking framework

This research addresses the new level-direction decomposition in the area of image watermarking as the further development of investigations. The main process of realizing a watermarking framework is to generate a watermarked image with a focus on contourlet embedding representation. The approach performance is evaluated through several indices including the peak signal-to-noise ratio and structural similarity, whereby a set of attacks are carried out using a module of simulated attacks. The obtained information is analyzed through a set of images, using different color models, to enable the calculation of normal correlation. The module of the inverse of contourlet embedding representation is correspondingly employed to obtain the present watermarked image, as long as a number of original images are applied to a scrambling module, to represent the information in disorder. This allows us to evaluate the performance of the proposed approach by analyzing a complicated system, where a deci-sion making system is designed to find the best level and the corresponding direction regarding contourlet embedding represen-tation. The results are illustrated in appropriate level-direction decomposition. The key contribution lies in using a new integration of a set of subsystems, employed based upon the novel mechanism in contourlet embedding representation, in association with the decision making system. The presented approach is efficient compared with state-of-the-art approaches, under a number of serious attacks. A number of benchmarks are obtained and considered along with the proposed framework outcomes. The results support our ideas.

A robust watermarking framework for High Efficiency Video Coding (HEVC) - Encoded video with blind extraction process

We propose a robust watermarking framework with a blind extraction process for HEVC encoded video, where a readable watermark is embedded invisibly in intra predicted blocks of the HEVC encoded vid...