Intelligent Watermarking Research Articles

An Intelligent Watermarking Technique for Secured Medical Images with Patient Health Document

An Intelligent Watermarking Technique for Secured Medical Images with Patient Health Document

Hardware realisation of an intelligent medical image watermarking

Digital intelligence features the competence to transmit data at a swifter measure with a substantially superior computing relaxation. Hindrance to this is the up rise of copyright transgression problems. In digital watermarking, novel consumer embeds a distinguishable imprint in the digital medium for eschewing franchise breach. A tunnelled version of the aforementioned methodology is medical image watermarking which primarily focuses on embedding of the watermark in a concerned medical pictorial. The writers have concerned on the realm of intelligent medical image watermarking. Medicative images like USG, CT scan, MRI and X-rays are utilised. An inkling of shovelling a masked image in juxtaposition to the novel medical paradigm has been insinuated. The binary mask used is primarily sectioned according to a fixed ratio and in accordance with a predefined intelligent schematic, the watermark bits are hidden in only a part of the sections to prevent distortion of the information pixels in the medical image. The entrenchment and excerpting epistemologies have been operationalised utilising FPGA. The image testing parameter outcomes are discernibly persistent. Furthermore, an analogy of the proposed approach against few germane methods ensures the supremacy of the technique put forward.

A DCT domain smart vicinity reliant fragile watermarking technique for DIBR 3D-TV

This work presents a vicinity reliant intelligent fragile watermarking scheme for depth image-based rendering technique used for three-dimensional television. Depth map of a centre image is implicitly inserted in the block-based discrete cosine transform (DCT) of the same using an aggregate, which also takes into account the presence of its neighbourhood blocks. Based upon the parity of a Boolean operation on the aggregate, parity is modulated which implicitly embeds the watermark. Genetic algorithm is then utilized to select the appropriate frequency bands in the DCT domain to become eligible for watermark embedding based on imperceptibility requirements. Experimental results demonstrate the usefulness of the proposed scheme in terms of its resistance against a set of fragile watermarking attacks and its ability to detect and localize tempering attempts.

LMDGW: a novel matrix based dynamic graph watermark

Android platform induces an open application development framework to attract more developers and promote larger market occupations. However, the open architecture also makes it easier to reverse engineering, which results in the property loss for both developers and companies, and increases the risks of mobile malicious code. Therefore, copyright protection for android application is of significant importance. A class of copyright protection methods are based on Android watermark. Different from static watermark, the process of embedding and extracting of dynamic graph watermark (DGW) is based on the function path and operation process of the app, which has better concealment. In this paper, we proposed a late-model dynamic watermark based on matrix, called “LMDGW”. This method is proposed to overcome the shortcoming of unintuitive and vulnerable property of traditional numberal DGW. We encode a matrix with low rank into a watermark graph, and embed the graph construction statements into smali code. With the containing of sensitive block characteristics, LMDGW is able to perceive and locate the changes in the specific block. Besides, LWDGW has great performance in tamper-proof attacks. Experiment results and analysis justified that LMDGW has great data rate and robust, and is available in sensitive code locating. LMDGW is proved to be an intelligent watermarking scheme, and is enlightening for intelligent security.

Intelligent Watermarking Scheme for image Authentication and Recovery

Recently, researchers have proposed semi-fragile watermarking techniques with the additional capability of image recovery. However, these approaches have certain limitations with respect to capacity, imperceptibility, and robustness. In this paper, we are proposing two independent watermarks, one for image recovery and the other for authentication. The first watermark (image digest), a highly compressed version of the original image itself, is used to recover the distorted image. Unlike the traditional quantisation matrix, genetic programming based matrices are used for compression purposes. These matrices are based on the local characteristics of the original image. Furthermore, a second watermark, which is a pseudo-random binary matrix, is generated to authenticate the host image precisely. Experimental results show that the semi-fragility of the watermarks makes the proposed scheme tolerant of JPEG lossy compression and it locates the tampered regions accurately.

Multiple Content Adaptive Intelligent Watermarking Schemes for the Protection of Blocks of a Document Image

Multiple Content Adaptive Intelligent Watermarking Schemes for the Protection of Blocks of a Document Image

Intelligent Watermarking for High-Capacity Low-Distortion Data Embedding

Image watermarking intends to hide secret data for the purposes of copyright protection, image authentication, data privacy, and broadcast monitoring. The ultimate goal is to achieve highest embedding capacity and lowest image distortion. In this paper, we present an intelligent watermarking scheme which can automatically analyze an image content to extract significant regions of interest (ROIs). A ROI is an area involving crucial information, and will be kept intact. The remaining regions of non-interest (RONIs) are collated for embedding watermarks, and will be ranked according to their entropy fuzzy memberships into a degree of importance. They are embedded by different amounts of bits to achieve optimal watermarking. The watermark is compressed and embedded in the frequency domain of the image. Experimental results show that the proposed technique has accomplished high capacity, high robustness, and high PSNR (peak signal-to-noise ratio) watermarking.

Intelligent Image Watermarking based on Handwritten Signature

With the growth of digital technology over the past decades, the issue of copyright protection has become especially important. Digital watermarking is a suitable way of addressing this issue. The main problem in the area of watermarking, is the balance between image transparency and resistance to attacks after watermarking, where an increase in either one of them will always cause a decrease in the other. Providing statistical and intelligent methods, is the most common way of optimizing resistance and transparency. In this paper, the intelligent method of genetic algorithm (GA) in watermarking will be examined and also the results of using this method will be compared with the results of a statistical SVD-based method. Also, by combining the issues of watermarking and authentication, a relatively higher security in these two issues can be achieved. In this scheme, the security of watermarking increases through the provision of a new method which is based on the combination of image watermarking with a person's handwritten signature. It must be mentioned that the section of signature recognition is implemented using neural networks. The results from implementing these two methods show that in this area, intelligent methods have a better performance compared to statistical methods. This method can also be used for tasks like passport or national identity card authentication.

Optimal image watermarking scheme based on chaotic map and quaternion wavelet transform

In this paper, an intelligent watermarking scheme optimized by adaptive differential evolution (ADE) is proposed based on chaotic map and quaternion wavelet transform (QWT). Watermark is first scrambled by a piecewise linear chaotic map to increase security and then inserted into the real QWT amplitude coefficients intelligently. ADE algorithm is explored to optimize the watermarking parameter (i.e., scaling factors) automatically. The experimental results demonstrate the high robustness against common signal processing operations, especially geometrical distortions of the proposed algorithm. Comparison results also indicate the superiority of the proposed algorithm over the existing watermarking algorithm.

Securing high resolution grayscale facial captures using a blockwise coevolutionary GA

In biometric systems, reference facial images captured during enrollment are commonly secured using watermarking, where invisible watermark bits are embedded into these images. Evolutionary Computation (EC) is widely used to optimize embedding parameters in intelligent watermarking (IW) systems. Traditional IW methods represent all blocks of a cover image as candidate embedding solutions of EC algorithms, and suffer from premature convergence when dealing with high resolution grayscale facial images. For instance, the dimensionality of the optimization problem to process a 2048×1536 pixel grayscale facial image that embeds 1 bit per 8×8 pixel block involves 49k variables represented with 293k binary bits. Such Large-Scale Global Optimization problems cannot be decomposed into smaller independent ones because watermarking metrics are calculated for the entire image. In this paper, a Blockwise Coevolutionary Genetic Algorithm (BCGA) is proposed for high dimensional IW optimization of embedding parameters of high resolution images. BCGA is based on the cooperative coevolution between different candidate solutions at the block level, using a local Block Watermarking Metric (BWM). It is characterized by a novel elitism mechanism that is driven by local blockwise metrics, where the blocks with higher BWM values are selected to form higher global fitness candidate solutions. The crossover and mutation operators of BCGA are performed on block level. Experimental results on PUT face image database indicate a 17% improvement of fitness produced by BCGA compared to classical GA. Due to improved exploration capabilities, BCGA convergence is reached in fewer generations indicating an optimization speedup.

Proposed an Intelligent Watermarking in GIS Environment

GIS (Geographic Information System) has been used in military and commercial applications for many years. The data of GIS are very expensive. So, it is very important to prevent any illegal use of these data. Digital watermarking can provide potential solution. As we know, in most of the applications of digital watermarking, the watermark is used to protect the copyright of digital product. In the other word, the product (cover) is important. According to this fact, we presented 2D vector map watermarking for GIS digital map based on a new watermarking concept called intelligent watermarking. The need of this concept is increased in the last years to protect the wide range of digital maps data transmitted via computer networks. Briefly, the scheme depends on the feature of the cover. The embedding of the watermark can be done in several steps. First extracting some features from the original digital vector map. Second combine these features with external watermark in order to get the intelligent watermark. Third embed the intelligent watermark in the vector map to get the watermarked vector map. The extracting of the watermark can be done in several steps. First, extract the intelligent watermark from the watermarked digital vector map. Second decompose the intelligent watermark into features and external watermark. Third reverse the extracted features to features of watermarked vector map. So, if the watermarked vector map is attacked, this watermarked vector map will be distorted, otherwise will not be distorted. Our proposed scheme can be applied in all the other media.

DS-DPSO: A dual surrogate approach for intelligent watermarking of bi-tonal document image streams

Intelligent watermarking (IW) techniques employ population-based evolutionary computing in order to optimize embedding parameters that trade-off between watermark robustness and image quality for digital watermarking systems. Recent advances indicate that it is possible to decrease the computational burden of IW techniques in scenarios involving long heterogeneous streams of bi-tonal document images by recalling embedding parameters (solutions) from a memory based on Gaussian Mixture Model (GMM) representation of optimization problems. This representation can provide ready-to-use solutions for similar optimization problem instances, avoiding the need for a costly re-optimization process. In this paper, a dual surrogate dynamic Particle Swarm Optimization (DS-DPSO) approach is proposed which employs a memory of GMMs in regression mode in order to decrease the cost of re-optimization for heterogeneous bi-tonal image streams. This approach is applied within a four level search for near-optimal solutions, with increasing computational burden and precision. Following previous research, the first two levels use GMM re-sampling to recall solutions for recurring problems, allowing to manage streams of heterogeneous images. Then, if embedding parameters of an image require a significant adaptation, the third level is activated. This optimization level relies on an off-line surrogate, using Gaussian Mixture Regression (GMR), in order to replace costly fitness evaluations during optimization. The final level also performs optimization, but GMR is employed as a costlier on-line surrogate in a worst-case scenario and provides a safeguard to the IW system. Experimental validation were performed on the OULU image data set, featuring heterogeneous image streams with a varying levels of attacks. In this scenario, the DS-DPSO approach has been shown to provide comparable level of watermarking performance with a 93% decline in computational cost compared to full re-optimization. Indeed, when significant parameter adaptation is required, fitness evaluations may be replaced with GMR.

Rapid blockwise multi-resolution clustering of facial images for intelligent watermarking

Population-based evolutionary computation (EC) is widely used to optimize embedding parameters in intelligent watermarking systems. Candidate solutions generated with these techniques allow finding optimal embedding parameters of all blocks of a cover image. However, using EC techniques for full optimization of a stream of high-resolution grayscale face images is very costly. In this paper, a blockwise multi-resolution clustering (BMRC) framework is proposed to reduce this cost. During training phase, solutions obtained from multi-objective optimization of reference face images are stored in an associative memory. During generalization operations, embedding parameters of an input image are determined by searching for previously stored solutions of similar sub-problems in memory, thereby eliminating the need for full optimization for the whole face image. Solutions for sub-problems correspond to the most common embedding parameters for a cluster of similar blocks in the texture feature space. BMRC identifies candidate block clusters used for embedding watermark bits using the robustness score metric. It measures the texture complexity of image block clusters and can thereby handle watermarks of different lengths. The proposed framework implements a multi-hypothesis approach by storing the optimization solutions according to different clustering resolutions and selecting the optimal resolution at the end of the watermarking process. Experimental results on the PUT face image database show a significant reduction in complexity up to 95.5 % reduction in fitness evaluations compared with reference methods for a stream of 198 face images.

Fast intelligent watermarking of heterogeneous image streams through mixture modeling of PSO populations

In intelligent watermarking (IW), evolutionary computing (EC) is employed in order to automatically set the embedding parameters of digital watermarking systems for each image. However, the computational complexity of EC techniques makes IW unfeasible for large scale applications involving heterogeneous images. In this paper, we propose a dynamic particle swarm optimization (DPSO) technique which relies on a memory of Gaussian mixture models (GMMs) of solutions in the optimization space. This technique is employed in the optimization of embedding parameters of a multi-level (robust/fragile) bi-tonal watermarking system in high data rate applications. A compact density representation of previously-found DPSO solutions is created through GMM in the optimization space, and stored in memory. Solutions are re-sampled from this memory, re-evaluated for new images and have their distribution of fitness values compared with that stored in the memory. When the distributions are similar, memory solutions are employed in a straightforward manner, avoiding costly re-optimization operations. A specialized memory management mechanism allows to maintain and adapt GMM distributions over time, as the image stream changes. This memory of GMMs allows an accurate representation of the topology of a stream of optimization problems. Consequently, new cases of optimization can be matched against previous cases more precisely (when compared with a memory of static solutions), leading to considerable decrease in computational burden. Simulation results on heterogeneous streams of images indicate that compared to full re-optimization for each document image, the proposed approach allows to decrease the computational requirement linked to EC by up to 97.7% with little impact on the accuracy for detecting watermarks. Comparable results were obtained for homogeneous streams of document images.

A high throughput system for intelligent watermarking of bi-tonal images

Abstract In intelligent watermarking of document images, evolutionary computing (EC) techniques are employed in order to determine embedding parameters of digital watermarking systems such that the trade-off between watermark robustness and image fidelity is optimized. However, existing techniques for intelligent watermarking rely on full optimization of embedding parameters for each image. This approach does not apply to high data rate applications due to its high computational complexity. In this paper, a novel intelligent watermarking technique based on Dynamic Particle Swarm Optimization (DPSO) is proposed. Intelligent watermarking of bi-tonal image streams is formulated as a dynamic optimization problem. This population-based technique allows to evolve a diversified set of solutions (i.e., embedding parameters) to an optimization problem, and solutions from previous optimizations are archived and re-considered prior to triggering new optimizations. In such case, costly optimization may be replaced by direct recall of quasi identical solutions. Simulations involving the intelligent watermarking of several long streams of homogeneous PDF document images resulted in a decrease of computational burden (number of fitness evaluations) of up to 97.2% with a negligible impact on accuracy.

Using Watermarking Techniques to prove Rightful Ownership of Web Images

This article introduces intelligent watermarking scheme to protect Web images from attackers who try to counterfeit the copyright to damage the rightful ownership. Using secret signs and logos that are embedded within the digital images, the technique can investigate technically the ownership claim. Also, the nature of each individual image is taken into consideration which gives more reliable results. The colour channel used was chosen depending on the value of its standard deviation to compromise between robustness and invisibility of the watermarks. Several types of test images, logos, attacks and evaluation metrics were used to examine the performance of the techniques used. Subjective and objective tests were used to check visually and mathematically the solidity and weakness of the used scheme.

An intelligent watermarking method based on particle swarm optimization

Meerwald, Koidl, and Uhl (2009) pointed out that the method proposed in Lin et al. (2008) exists potential insecurity. This paper proposes an intelligent watermarking by invoking particle swarm optimization (PSO) technique in wavelet domain to overcome the revealed insecurity issue, furthermore resolve the conflict between imperceptibility and robustness of watermarking. In the proposed method, PSO is fused with the method proposed in Lin et al. (2008) (denoted SDWCQ) to avoid potentially insecurity in Lin et al. (2008). That is, the method of using the fixed block size in one subband and the permutation is unable to disguise which coefficients make up a block. The attacker can utilize the insecure property and analyze the significant difference between bipolar watermarks in Lin et al. (2008) to detect the embedded blocks, furthermore modify the significant difference, and result in unable to extract the watermark. In this paper, coefficients are randomly selected from different subbands to make up a block to promote the disguise. Performance analysis shows that the proposed algorithm obviously outperforms SDWCQ which does not use PSO.

BCH coding and intelligent watermark embedding: Employing both frequency and strength selection

This paper presents a novel approach of adaptive visual tuning of a watermark in Discrete Cosine Transform (DCT) domain. The proposed approach intelligently selects appropriate frequency bands as well as optimal strength of alteration. Genetic Programming (GP) is applied to structure the watermark by exploiting both the characteristics of human visual system and information pertaining to a cascade of conceivable attacks. The developed visual tuning expressions are dependent on frequency and luminance sensitivities, and contrast masking. To further enhance robustness, spread spectrum based watermarking and Bose–Chadhuri–Hocquenghem (BCH) coding is employed. The combination of spread spectrum sequence, BCH coding and GP based non-linear structuring makes it extremely difficult for an attacker to gain information about the secret knowledge of the watermarking system. Experimental results show the superiority of the proposed approach against the existing approaches. Especially, the margin of improvement in robustness will be of high importance in medical and context aware related applications of watermarking.

Intelligent Logo Watermarking Based on Independent Component Analysis

We present new intelligent logo watermarking based on independent component analysis (ICA) in which a binary logo watermark is embedded in a host image in a wavelet domain. To improve robustness, an image adaptive watermarking algorithm is applied by a stochastic approach based on a noise visibility function (NVF). The algorithm design, evaluation, and experimentation are described. Experimental results show that the logo watermark is perfectly extracted by ICA with excellent invisibility and with robustness against various image and digital processing operators and almost all compression algorithms such as Jpeg, jpeg 2000, SPIHT, EZW, and principal component analysis (PCA) based compression.