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High false positive face detection is a crucial problem which leads to low performance face recognition in surveillance system. The performance can be increased by reducing these false positives so that non-face can be discarded first prior to recognition. This paper presents a combination of two well-known algorithms, Adaboost and Neural Network, to detect face in static images which is able to reduce the false-positives drastically. This method utilizes Haar-like features to extract the face rapidly using integral image. A cascade Adaboost classifier is used to increase the face detection speed. Due to using only this cascade Adaboost produces high false-positives, neural network is used as the final classifier to verify face or non-face. For a faster processing time, hierarchical Neural Network is used to increase the face detection rate. Experiments on four different face databases, which consist more than one thousand images, have been conducted. Results reveal that the proposed method achieves about 93.34% of detection rate and 0.34% of false positives compared to original cascade Adaboost method which achieves about 98.13% of detection rate with 6.50% of false positives. The processed images size is 240 x 320 pixels. Each frame is processed at about 2.25 sec which is slights lower than the original method, which only takes about 0.82 sec.

Biometric technology plays a vital role for providing the security which is imperative part in secure system. Human face recognition is a potential method of biometric authentication. This paper presents a process of face recognition system using principle component analysis with Back propagation neural network where features of face image has been combined by applying face detection and edge detection technique. In this system, the performance has been analyzed based on the proposed feature fusion technique. At first, the fussed feature has been extracted and the dimension of the feature vector has been reduced using Principal Component Analysis method. The reduced vector has been classified by Back propagation neural network based classifier. In recognition stage, several steps are required. Finally, we analyzed the performance of the system for different size of the train database. The performance analysis shows that the efficiency has been enhanced when the feature extraction operation performed successfully. The performance of the system has been reached more than 92% for the adverse conditions.

Facial feature detection and extraction plays a significant role in face recognition. In this paper, we proposed a new approach for face feature description using support vector machine classifier. Searching image from the image contents is a major challenge in computer vision and pattern recognition. The textual label is assigned to the face feature according to the semantic meaning of the feature. The concept of the textual image description gives advantages to the user to search the image with the help of the visual attributes of the image. The face features like eyes, nose, and mouth are labeled with the visual attributes like small, normal and large.

Signatures are imperative biometric attributes of humans that have long been used for authorization purposes. Most organizations primarily focus on the visual appearance of the signature for verification purposes. Many documents, such as forms, contracts, bank cheques, and credit card transactions require the signing of a signature. Therefore, it is of upmost importance to be able to recognize signatures accurately, effortlessly, and in a timely manner. In this work, an artificial neural network based on the well-known Back-propagation algorithm is used for recognition and verification. To test the performance of the system, the False Reject Rate, the False Accept Rate, and the Equal Error Rate (EER) are calculated. The system was tested with 400 test signature samples, which include genuine and forged signatures of twenty individuals. The aim of this work is to limit the computer singularity in deciding whether the signature is forged or not, and to allow the signature verification personnel to participate in the deciding process through adding a label which indicates the amount of similarity between the signature which we want to recognize and the original signature. This approach allows judging the signature accuracy, and achieving more effective results.

Content Based Image Retrieval (CBIR) is a technique that enables a user to extract an image based on a query, from a database containing a large amount of images. A very fundamental issue in designing a content based image retrieval system is to select the image features that best represent the image contents in a database. In this paper, our proposed method mainly concentrated on database classification and efficient image representation. We present a method for content based image retrieval based on support vector machine classifier. In this method the feature extraction was done based on the colour string coding and string comparison. We succeed in transferring the images retrieval problem to strings comparison. Thus the computational complexity is decreases obviously. The image database used in our experiment contains 1800 colour images from Corel photo galleries.This CBIR approach has significantly increased the accuracy in obtaining results for image retrieval.

The effective content-based image retrieval (CBIR) needs efficient extraction of low level features like color, texture and shapes for indexing and fast query image matching with indexed images for the retrieval of similar images. Features are extracted from images in pixel and compressed domains. However, now most of the existing images are in compressed formats like JPEG using DCT (discrete cosine transformation). In this paper we study the issues of efficient extraction of features and the effective matching of images in the compressed domain. In our method the quantized histogram statistical texture features are extracted from the DCT blocks of the image using the significant energy of the DC and the first three AC coefficients of the blocks. For the effective matching of the image with images, various distance metrics are used to measure similarities using texture features. The analysis of the effective CBIR is performed on the basis of various distance metrics in different number of quantization bins. The proposed method is tested by using Corel image database and the experimental results show that our method has robust image retrieval for various distance metrics with different histogram quantization in a compressed domain.

Content based image retrieval technique is done by three primitive methods namely through color, shape and texture. This paper provides specified path to use these primitive features to retrieve the desired image. The technique by which we obtain the required image is CBIR. In CBIR first the HSV color space is quantified to obtain the color histogram and texture features. Using these components a feature matrix is formed. Then this matrix is mapped with the characteristic of global color histogram and local color histogram, which are analysed and compared. For the cooccurrence matrix between the local image and the images in the database to retrieve the image. For extracting shape feature gradient method is used here. Based on this principle, CBIR system uses color, texture and shape fused features to retrieve desired image from the large database and hence provides more efficiency or enhancement in image retrieval than the single feature retrieval system which means better image retrieval results.

The image descriptors based on multi-features fusion have better performance than that based on simple feature in content-based image retrieval (CBIR). However, these methods still have some limitations: 1) the methods that define directly texture in color space put more emphasis on color than texture feature; 2) traditional descriptors based on histogram statistics disregard the spatial correlation between structure elements; 3) the descriptors based on structure element correlation (SEC) disregard the occurring probability of structure elements. To solve these problems, we propose a novel image descriptor, called Global Correlation Descriptor (GCD), to extract color and texture feature respectively so that these features have the same effect in CBIR. In addition, we propose Global Correlation Vector (GCV) and Directional Global Correlation Vector (DGCV) which can integrate the advantages of histogram statistics and SEC to characterize color and texture features respectively. Experimental results demonstrate that GCD is more robust and discriminative than other image descriptors in CBIR

A novel content-based image retrieval (CBIR) schema with wavelet and color features followed by ant colony optimization (ACO) feature selection has been proposed in this paper. A new feature extraction schema including texture features from wavelet transformation and color features in RGB and HSV domain is proposed as representative feature vector for images in database. Also, appropriate similarity measure for each feature is presented. Retrieving results are so sensitive to image features used in content-based image retrieval. We address this problem with selection of most relevant features among complete feature set by ant colony optimization based feature selection. To evaluate the performance of our proposed CBIR schema, it has been compared with older proposed systems, results show that the precision and recall of our proposed schema are higher than older ones for the majority of image categories

In this letter, we propose a new adaptive weighted mean filter (AWMF) for detecting and removing high level of salt-and-pepper noise. For each pixel, we firstly determine the adaptive window size by continuously enlarging the window size until the maximum and minimum values of two successive windows are equal respectively. Then the current pixel is regarded as noise candidate if it is equal to the maximum or minimum values, otherwise, it is regarded as noise-free pixel. Finally, the noise candidate is replaced by the weighted mean of the current window, while the noise-free pixel is left unchanged. Experiments and comparisons demonstrate that our proposed filter has very low detection error rate and high restoration quality especially for high-level noise.

Image Enhancement is a process of improving the quality of an image by improving the contrast of the images. Images acquired underwater usually suffers from non-uniform illumination, low-contrast, motion blur effect due to turbulence in the flow of water, in under ocean environment, scattering of light from different particles of various sizes, diminished intensity and color level due to poor visibility conditions, suspended moving particles and so on. Due to all these factors, various types of noises occur and to reduce the effects arising out of these factors, a number of methods are required to be incorporated to improve the quality of underwater images. The present paper shows a comparative study of the various image enhancement techniques used for enhancing underwater images and introduces a suitable novel hybrid method for improving the image quality.

A novel edge-preserving image decomposition based on saliency map is proposed to avoid halo phenomena in traditional image decompositions. Our method uses feature of saliency map that emphasizes salient edges in image to determine whether a region should be retained or processed, and then blur the image adaptively according to the local saliency average gradient. Experiments show that the proposed decomposition performs well in smoothing and enhancement. We compare our results with the guided filter and bilateral filter, and demonstrate a variety of applications including HDR and multi-scale enhancement.

स्टेग्नोग्राफ़ी is a data hiding technique that is widely used in various information securing applications. स्टेग्नोग्राफ़ी transmits data by hiding the existence of the message so that a viewer cannot identify the transmission of message and hence not able to decrypt it. This work proposes a data securing technique that is used for hiding multiple color images into a single color image using the Discrete Wavelet Transform. The cover image is split up into R, G and B planes. Secret images are embedded into these planes. An N-level decomposition of the cover image and the secret images are done and some frequency components of the same are combined. Secret images are then extracted from the stego image. Here, the stego image obtained has a less perceptible changes compared to the original image with high overall security.

In this paper, we introduce a ghost-free High Dynamic Range imaging algorithm for obtaining ghost-free high dynamic range (HDR) images. The multiple image fusion based HDR method work only on condition that there is no movement of camera and object when capturing multiple, differently exposed low dynamic range (LDR) images. The proposed algorithm makes three LDR images from a single input image to remove such an unrealistic condition. For this purpose a histogram separation method is proposed in the algorithm for generating three LDR images by stretching each separated histogram. An edge-preserving denoising technique is also proposed in the algorithm to suppress the noise that is amplified in the stretching process. In the proposed algorithm final HDR image free from ghost artifacts in dynamic environment because it self-generates three LDR images from a single input image. Therefore, the proposed algorithm can be use in mobile phone camera and a consumer compact camera to provide the ghost artifacts free HDR images in the form of either inbuilt or post-processing software application.

This paper presents an efficient algorithm for solving a balanced regularization problem in the frame-based image restoration. The balanced regularization is usually formulated as a minimization problem, involving an 2 data-fidelity term, an 1 regularizer on sparsity of frame coefficients, and a penalty on distance of sparse frame coefficients to the range of the frame operator. In image restoration, the balanced regularization approach bridges the synthesis-based and analysis-based approaches, and balances the fidelity, sparsity, and smoothness of the solution. Our proposed algorithm for solving the balanced optimal problem is based on a variable splitting strategy and the classical alternating direction method. This paper shows that the proposed algorithm is fast and efficient in solving the standard image restoration with balanced regularization. More precisely, a regularized version of the Hessian matrix of the 2 data-fidelity term is involved, and by exploiting the related fast tight Parseval frame and the special structures of the observation matrices, the regularized Hessian matrix can perform quite efficiently for the frame-based standard image restoration applications, such as circular deconvolution in image deblurring and missing samples in image inpainting. Numerical simulations illustrate the efficiency of our proposed algorithm in the frame-based image restoration with balanced regularization

Image deblurring is one of the fundamental problems in the इमेज प्रोसेसिंग and computer vision fields. In this paper, we propose a new approach for restoring images corrupted by blur and impulse noise. The existing methods used to address this problem are based on minimizing the objective functional, which is the sum of the L1-data fidelity term, and the total variation (TV) regularization term. owever, TV introduces staircase effects. Thus, we propose a new objective functional that combines the tight framelet and TV to restore images corrupted by blur and impulsive noise while mitigating staircase effects. The minimization of the new objective functional presents a computational challenge. We propose a fast minimization algorithm by employing the augmented Lagrangian technique. The experiments on a set of image deblurring benchmark problems show that the proposed method outperforms previous state-of-the-art methods for image restoration.

In this paper, a novel and simple restorationbased fog removal approach is proposed. Here, we proposed an approach which is based on gamma transformation method and median filter. Transmission map is refined by the gamma transformation method. Then obtained transmission map is smoothed by a median filter. Qualitative and quantitative analysis demonstrate that proposed algorithm performs well in comparison with bilateral filtering. It can handle color as well as gray images. Proposed algorithm, due to its speed and ability to improve visibility, may be used in many systems, ranging from tracking and navigation, surveillance, consumer electronics, intelligent vehicles to remote sensing.

This paper deals with estimation of parameters for motion blurred images. The objectives are to estimate the length (L) and the blur angle () of the given degraded image as accurately as possible so that the restoration performance can be optimised. Gabor filter is utilized to estimate the blur angle whereas a trained radial basis function neural network (RBFNN) estimates the blur length. Once these parameters are estimated the conventional restoration is performed. To validate the proposed scheme, simulation has been carried out on standard images as well as in real images subjected to different blur angles and lengths. The robustness of the scheme is also validated in noise situations of different strengths. In all situations, the results have been compared with standard schemes. It is in general observed that the proposed scheme outperforms its counterparts in terms of restoration parameters and visual quality.

The restoration of motion blurred images is a hot shot in the field of इमेज प्रोसेसिंग. In this paper, The degradation model of motion blurred images is clarified, and the estimation of the PSF parameter as well as its algorithm is presented. Frequency spectrum and Radon transition are used for the length and angle calculation, then Wiener filtering and Inverse filtering are used for recovering the noisy and motion blurred images. The matrix conjugate gradient is used to process the images which are processed by Wiener filtering and Inverse filtering. These simulation results indicate that matrix conjugate gradient is effective and promising for their recovery.

A new model for impulse noise reduction with a recursive and noise-exclusive fuzzy switching median filter is proposed. The filter uses a S-type membership function for fuzzifying the noisy input variable and then estimates a correction label for it that aims at canceling the noisy effect. The fuzzification process provides better smoothing and generalization that improve the performance of the filter. The recursive and noise-exclusive operations further enhance the noise reduction capability of the filter. The recursive operation replaces the current pixel with the filtered pixel(s) and the noise-exclusive filtering uses only noise-free pixel(s) of the working window. The net effect of the filtering process thus preserves the sharp edges and fine details of the image in a more effective manner. The superiority of the proposed model to others in removing high density noise is established both quantitatively and qualitatively with various benchmarks and real-time test data sets. With the incorporation of the functionalities like fuzzy reasoning and noise-exclusive operations in the filtering process, the model becomes more efficient and fast. The applicability of the model thus opens ample scopes for hardware implementation in many electronics products that deal with images and videos.

Satellite imaging is being the most attractive source of information for the governmental agencies and the commercial companies in last decade. The quality of the images is very important especially for the military or the police forces to pick the valuable information from the details. Satellite images may have unwanted signals called as noise in addition to useful information for several reasons such as heat generated electrons, bad sensor, wrong ISO settings, vibration and clouds. There are several image enhancement algorithms to reduce the effects of noise over the image to see the details and gather meaningful information. Many of these algorithms accept several parameters from the user to reach the best results. In the process of denoising, there is always a competition between the noise reduction and the fine preservation. If there is a competition between the objectives then an evolutionary multi objective optimization (EMO) is needed. In this work, the parameters of the image denoising algorithms have been optimized to minimize the trade-off by using improved Strength Pareto Evolutionary Algorithm (SPEA2). SPEA2 differs from the other EMO algorithms with the fitness assignment, the density estimation and the archive truncation processes. There is no single optimal solution in a multi objective problems instead there is a set of solutions called as Pareto efficient. Four objective functions, namely Mean Square Error (MSE), Entropy, Structural SIMilarity (SSIM) and Second Derivative of the image, have been used in this work. MSE is calculated by taking the square of difference between the noise free image and the deniosed image. Entropy is a measure of randomness of the content of difference image. The lower entropy is the better. The second derivate of an image can be achieved by convolving the image with the Laplacian Mask. SSIM algorithm is based on the similarities of the structures on the noise free image and the structures of the denoised image. For the image enhancement algorithms, Insight Segmentation and Registration Toolkit (ITK) is selected. ITK is an open source project and it is being developed in C++ to provide developers with a rich set of applications for image analysis. It includes tens of image filters for the registration and segmentation purposes. In this work, Bilateral Image Filter is evaluated in the field of satellite imaging for the noise removal process. The evaluated filter receives two parameters from the user side within their predefined ranges. Here, SPEA2 algorithm takes the responsibility to optimize these parameters to reach the best noise free image results. SPEA2 algorithm was implemented in Matlab and executable files of image filter were called in Matlab environment. The results of the work were represented graphically to show the effectiveness of selected method.

In this paper, we propose the method for recognizing the degree of snowfall automatically even if most of backgrounds are covered with snow and the visibility is low by fog. When it snows heavily, fog often occurs simultaneously. Moreover, falling snow grains have low contrast to the background covered with white snow. In order to deal with these situations, the proposed method makes an input image clear by fog removal. We propose the novel fog removal method which can be applied to not only the usual scene but also the heavy snowy scene. This method can remove the influence of fog without depending on the grade of visibility because the degree of fog removal is changed dynamically. The degree of snowfall is estimated from the quantity of falling snow grains, which are extracted from the difference between the present defogged image and the background image created by the three-dimensional median. Experiments conducted under various degrees of snowfall have shown the effectiveness of the proposed method.

In this paper, we propose a new transmission model using LO norm for image fog removal. In the prior work, the bilateral filter was used to reduce the halo artifacts. However, it is only a local optimization. Hence, we observe non-zero gradients to develop the gradient smoothing method for global control. The proposed model then locates significant edges to highlight the prominent parts of an image. Experimental results show the effectiveness of the proposed model for defogging.

This paper introduces the Automated Two-Dimensional K-Means (A2DKM) algorithm, a novel unsupervised clustering technique. The proposed technique differs from the conventional clustering techniques because it eliminates the need for users to determine the number of clusters. In addition, A2DKM incorporates local and spatial information of the data into the clustering analysis. A2DKM is qualitatively and quantitatively compared with the conventional clustering algorithms, namely, the K-Means (KM), Fuzzy C-Means (FCM), Moving K-Means (MKM), and Adaptive Fuzzy K-Means (AFKM) algorithms. The A2DKM outperforms these algorithms by producing more homogeneous segmentation results.

K-means algorithm and ant clustering algorithm are all traditional algorithms. The two algorithms can complement each other. The combination of two algorithms will improve clustering’s accuracy and speed up algorithm’ convergence. Tests prove hybrid clustering algorithm is more effective than each above-mentioned algorithm. Especially, the new algorithm has good results in image segmentation.

In medical इमेज प्रोसेसिंग, interactive image segmentation is an important part, because it can obtain accurate segment results with less human effort compared with manual scribing. We proposed an improved algorithm of maximal similarity based region merging, compared with the algorithm proposed in our algorithm use SLIC superpixels segmentation to obtain presegmented regions, using SLIC superpixles, it is easy to control the number of presegmentation regions. We also introduce the texture features differeces while rigion merging, so we can obtain the accuracy of similarity measurement. Experimental results show that our algorithm can obtain comparable results.

ion Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2- D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.

The basic developing a digital image watermarking (DIW) objective approach is to satisfy both robustness and imperceptibility requirement. Digital watermarking (DW) appears as a capable means of protection multimedia contents for example authentication and protection. In this paper, we define SVD based DW approach for robust watermarking of digital images (DI) for copyright protection. In proposed research, a new and robust DW approach is begin in which a mixture of DWT and FFT along with SVD is using. Because of the usage of this combination of three approaches in our proposed work, it enhances imperceptibility and robustness of extracted image. At first DWT-FFT-SVD hybrid method is using for embedding the watermark then reverse algorithm is using for extraction. The protection of the proposed way is improving by means of utilizing a different wavelet operates. We also demonstrate the good correlation between extracted and embedded watermark with the help of experimental outcomes. One of the major benefits of the proposed process is the robustness of the approach on wide attacks set. Analysis and experimental outcomes present much enhanced performance of the proposed way in comparison with the pure SVD-based watermarking and the approach without using some wavelet function. The results are compared with Base Work in which single level DWT-SVD combination is taken for watermarking for copy right protection. It is shown through PSNR (Peak signal-to-noise ratio) that it provided a very high imperceptibility. Experimental outcomes confirm that the proposed manner provides good watermarked images (WI) quality.

 

 

CHAPTER 1

INTRODUCTION

 

Internet has become indispensable and thus the security and the privacy issue have come to the fore of the computing fraternity. These issues should be tended to with most extreme earnestness and largest amount of commitment.

Watermarking addresses the protection and security issues. Watermarking has useful not just in secure but also in resolving many privacy and copyright problem, which became one of the most contentious problem while the internet expansion. This proposal explores the state of art define a novel approach to tackle elephant in the room.

Watermarking technique can be isolated on the area based premise, archive based, Perception based and application based. In spatial domain watermarking (SDW), watermark is embedded through change pixels value of the host image/ video directly.

Watermarking technology has spread widely around the globe due to the world-wide distribution of multimedia content through the various methods of networking [1][2][3]. Intellectual Property rights, like copyrights or the photographic rights of content should be strictly protected in these various situations. We proposed “optical watermarking” technology as one solution to this problem that uses illumination containing invisible watermarked information. When the illumination is projected onto real objects and the photographs are taken by digital cameras, the digital data on the pictures also contain invisible watermarked information. One of the unique features of this technology is that it can be used to embed invisible DW information in the image data of real objects with spatially modulated illumination. Therefore, this technology has different features from conventional digital watermarking technologies. When conventional DW technologies are used, images of objects with no copyright protection, such as pictures in museums painted by famous artists, cannot be secure from being unlawfully photographed. This is because that watermarked information has to be embedded into the image data before they are used in conventional watermarking technologies. Optical watermarking technology can offer a solution to resolve such difficult situations.

Digital watermark can be defined as a signal completely embedded into digital data(audio, pictures, video, and textual content) that may be detected or extracted later by the use of computing operations with a view to make assertions in regards to the data. The watermark image is embedded with the host image in such way so that watermark image remains hidden and is inseparable. But in true facts the process of DW is divided into three stages: first one is the Embedding stage, second is the Distortion\assault Stage and the last one is Detection/Retrieval stage.

In actual fact working of DIW can be separated in three stages: Embedding Stage, Distortion/assault Stage, and Detection/Retrieval Stage. The embedding step is the 1^st step where the watermark is added with the original image with the aid of utilizing the embedding watermark algorithm and the secret key so that by following some algorithm watermarked picture can be generated. 2nd stage, when the information is passed on the network. At this stage either some attack are carried out or some noise is introduced on to the watermarked Image. At the finding phase, the watermark is detected or removes with the aid of the dedicated detector from embedded image by using few detection algorithms and by way of utilizing secret key (SK).  In addition to this, noise is also detected [4].

According to our reviews we have seen that Copyright infringement, information misappropriation and mishandle brings about diminishing development of mixed media organized administrations. That means, as applications in the areas of multimedia communications and multimedia networking increases, the problem of theft and distribution of intellectual property also increases. Thus authentication, security and copyright protection of digital multimedia components becomes an important concern in the digital world and this can be done using watermarking technique. For hiding the information two types of watermarking algorithms are available: spatial domain and transform domain. The basic ideology in the majority of the transform domain watermarking design is to alter the transform coefficients based on the bits in watermark image. The bulk of the watermarking schemes based on frequency domain works with DCT, FFT and DWT. However, SVD is the leading numerical analysis technique these days.

DW fast developing research digitized images area, audio and video has urged the copyright protection requirement, which can be used to generate confirmation towards any illegal attempt to either manipulate or reproduce them as a way to modify their identity.  Computerized watermarking is a developing innovation gathering, for instance flag preparing, cryptography, likelihood hypothesis and stochastic hypothesis, organize technique, algorithm design and other approach.

Jalil and Mirza (2010), Bandyopadhyay and Paul (2010). The fundamental characteristics of DW are confidential into most important grouping as follows.

 

A.  Characteristics of Digital watermarking

 

There are various essential qualities that watermark can show, Jalil and Mirza (2010), Bandyopadhyay and Paul (2010). The DW characteristics are classified into huge group as follows.

• Robustness: The watermark should be competent to resist after traditional operations on IP for example picture cropping, compression, transformation and so forth
• Imperceptibility: The WI ought to seem like same as unique picture to the normal eye. The observer cannot find that watermark is embedded in it.
• Security: An unauthorized anybody cannot detect, retrieve or regulate embedded watermark.

• Transparency: Straightforwardness identifies with human tangible properties. A straightforward watermark causes no ancient rarities or highlight misfortune.
• Capacity: Capacity defines how several knowledge bits can be fixed. It also addresses possibility of embedding multiple watermarks in one document in parallel. Capacity needs always effort against two other important need that is, robustness and imperceptibility (Fig 4). A higher limit is commonly found to the detriment of either vigor quality or subtlety, or both.

B.  Properties of Digital watermarking

 

Watermarking process has few foremost famous properties. Some of these properties like

• Effectiveness: It is the possibility that knowledge in a watermarked image will be properly detected; it ideally required this probability to be 1 [6].

 

• Image fidelity: process of the watermarking that changes an original image to add information to it; therefore it certainly affects the image quality. We want to preserve this poverty of the image exceptional to a minimum, so no apparent variation within the picture fidelity will also be observed [6].

 

• Payload size: In which each watermarked work is used to include understanding. The knowledge size is often necessary as numerous systems need a comparatively huge payload to be embedded in a covered work [6].

 

• False positive rate: It is quite a lot of digital works which are well-known to have a watermark embedded when without a doubt they have got no watermark embedded. This ought to be held low to watermarking frameworks [6].

 

• Robustness: In which a watermarked work is more than a few at the time of its lifetime, both by means of transmission over a lossy channel or many malicious attacks that try to eliminate the watermark or create it undetectable. A powerful watermark ought to have the capacity to withstand added substance trimming, Gaussian commotion, pressure and printing, checking, scaling, revolution and their operations [6].

An application based on the DW.

The basic benefit of pixel based approach are that they are conceptually easily and have very low computational complexities and therefore are extensively used in the video watermarking where ongoing execution is an essential concern. Notwithstanding, they likewise show some real impediments.

In the frequency domain, watermark is embedded for the watermarking mechanism robustness. There are three main methods of data transmission in frequency domain as SVD FFT and DWT. The basic strength offered through transforming domain approach is that they can take benefit of special properties of alternate domains to address limitations of pixel-based strategies or to bolster extra elements. For the most part, change area techniques require higher computational time. In change space method, the watermark is inserted distributive in general area of unique information. Have video is initially changed over into recurrence area by change systems. The changed space coefficients are then adjusted to store the watermark data. The reverse change is at long last connected keeping in mind the end goal to get the watermarked video. As indicated by the human recognition watermarking is partitioned into two sections noticeable watermark media and undetectable watermark media. For efficient watermarking approach required to be imperceptible (watermark should not degrade multimedia quality), robust (after using attacks multimedia quality should not be degrade), and protect from numerious attacks. In Most of the watermarking approach, watermark is embedded into frequency domain instead of the spatial domain for the watermarking mechanism robustness. SVD, FFT and DWT are three basic approach of data transformation in this domain. The basic strength offered through transforming domain approach is that they can take benefit of special alternate domains properties to address limitations of pixel-based way or to maintain extra features.

On the whole, become area ways require higher computational time. In change space system, the watermark is inserted distributive in general area of unique information. Here, the host video is initially changed over into recurrence space by change methods. The changed area coefficients are then modified to store the watermark data. [7]

 

1. Types of Watermarking

 

• Visible: The watermark is noticeable that may be a textual content or a symbol. It's used to establish the owner [8].
• Invisible: The watermark is installed into the picture in a manner that it can't be seen by human eye. It is utilized to secure the picture confirmation and furthermore keep it from being duplicated. Invisible watermark may also be additional categorized into three forms:
• Robust Watermark: It is ambitions to embed information in a file that are not able to be quite simply destroyed.  They are intended to oppose any controls that might be experienced. All applications where security is the principle issue utilize hearty watermarks.
• Fragile Watermark: They are designed with very low robustness. It is used to confirm the reliability of objects.
• Public and Private Watermark: They're differentiated in response to the secrecy requisites for the important thing used to embed and retrieve watermarks. In the event that the first picture is not known amid the discovery procedure then it is known as an open or a visually impaired watermark and if the first picture is known it is known as a non-blind watermark or a private watermark

 

1. Watermarking Applications

Watermarking technologies is connected in each advanced media though security and proprietor recognizable proof is required [9]

• Owner Identification: The utilization of watermarking to which he created is to distinguish the proprietor of any media. Some paper watermark is effortlessly expelled by some little practice of assailants. So the advanced watermark was presented. In that the watermark is the interior piece of computerized media with the goal that it can't be effortlessly recognized and expelled.
• Copy Protection: Unlawful replicating is likewise avoiding by watermarking with duplicate ensure bit. This insurance requires duplicating gadgets to be coordinated with the watermark distinguishing hardware.
• Medical Applications: Medicinal media and reports additionally carefully confirmed, having the data of patient and the meeting specialists. These watermarks can be both unmistakable and imperceptible. This watermarking helps specialists and restorative applications to check that the reports are not altered by unlawful means.
• Data Authentication: Confirmation is the procedure of recognize that the got substance or information ought to be correct as it was sent. So for that reason sender inserted the computerized watermark with the host information and it would be removed at the recipients end and checked.
• Fingerprinting: A fingerprinting is a method by which a work can be relegated a one of a kind ID by putting away some computerized data in it as watermark. Identifying the watermark from any illicit duplicate can prompt to the recognizable proof of the individual who has released the first substance. In silver screen corridors the motion pictures are played carefully through satellite which has the watermark having theater recognizable proof so if theater distinguishing Proof so if theater distinguishing proof identified from a pirated copy then action towards a theater can be taken.
• Copyright protection: It is used to identify and protect official document ownership [10].
• Digital right management: It can be utilized for portrayal, distinguishing proof, exchanging, securing, observing and following of all types of utilizations over substantial and intangible assets [10].
• Tamper proofing: It is used for fragile in nature [11].
• Broadcast monitoring: In which application the quantity of TV and radio channels conveying content has eminently extended [11].
• Image and content authentication: In this application the objective is to detect modification of the data. The picture qualities, for instance its edges, are inserted and contrasted and introduce pictures for contrasts.

 

1. Watermarking Attacks

There are quite a lot of viable malicious intentional or unintended attacks that a watermarked object is likely to field to.  The availability of wide range of इमेज प्रोसेसिंग soft ware’s made it possible to perform attacks on the robustness of the watermarking systems. The point of these assaults is keep the watermark from playing out its planned reason. [12].

• Removal Attack: Expulsion attacks mean to expel the watermark information from the watermarked protest. Such assaults misuse the way that the watermark is generally an added substance clamor flag display in the host flag.
• Interference attack: Impedance assaults are those which add extra clamor to the watermarked question. Lossy pressure, quantization, plot, denoising, demodulation, averaging, and clamor tempest are a few cases of this classification of attacks.
• Geometric attack: All manipulations that impact the geometry of the image equivalent to flipping, rotation, cropping, and so forth. Will have to be detectable. A trimming attacks from the right-hand side and the base of the picture is a case of this attack.
• Low pass filtering attack: A low pass filtering is done over the WI and it results in a difference map composed of noise.
• Forgery attack: The falsification attacks that outcome in protest inclusion and erasure, scene foundation changes are all equivalent to substitution.
• f) Security Attack: Specifically, if the watermarking calculation is known, an assailant can additionally attempt to perform changes to render the watermark invalid or to evaluate and alter the watermark. For this situation, we discuss an attack on security. The watermarking calculation is viewed as secure if the inserted data can't be pulverized, recognized or fashioned.
• Protocol Attack: The convention attacks do neither go for decimating the inserted data nor at incapacitating the identification of the implanted data (deactivation of the watermark). As opposed to that, they exploit semantic shortages of the watermark„s usage. Thusly, a strong watermark must not be invertible or to be replicated. A duplicate attack, for example, would purpose at copying a watermark from one media into a different without capabilities of the secret key.
• Cryptographic attacks: Cryptographic attacks deal with the cracking of the security. For instance, finding the secrete watermarking key utilizing comprehensive beast constrain technique is a cryptographic attack. Another example of this type of attack is the oracle attack [13]. In the oracle attack, a non-watermarked question is made when an open watermark locator gadget is accessible. These attacks are similar to the attacks used in cryptography.

 

1. Watermarking Techniques

Several watermarking methods are available. But, these methods are generally found in sound watermarking.

• Discrete Wavelet Transform:

The DWT is just something of filters. You will get two filters included, one could be the “wavelet filter”, and the other could be the “scaling filter”. The wavelet filtration is just a large go filtration, as the scaling filtration is just a low go filter. Determine 2 reveals workflow of DWT. A benefit of DWT over various transforms is it enables great localization equally in time and spatial frequency domain. Since that organic multiresolution nature, wavelet development schemes are especially ideal for applications where scalability and tolerable destruction are important. DWT is preferred, because it provides equally a parallel spatial localization and a volume distribute of the watermark within the host picture. The hierarchical house of the DWT offers the possibility of analyzing an indication at numerous promises and orientations.

It is normally applied in so many techniques because of its time/frequency characteristics. Right here a picture is handed by way of sequence of low pass and high pass filters which decompose the picture into sub bands of horizontal element of the image; the backside correct block involves high frequency of usual picture.

Wavelet transforms (WT) are very useful due to its extracted features. WT are useful to provide better results in recognition.

In DIW to insert watermarks in different frequency sub-bands. According to Mallat algorithm [14] the DWT a given signal x(t) in the Vj−1 (Vj +Wj) is given by Eq. 1

x(t) = ∑ k aj−1,k2(−j+1)/2φ(2−j+1 t −k) (1)

whereVj, is a sub-space and Wj is the Orthogonal complement of Vj. Eq. 2 shows the result of signal projected on to the Vj and Wj spaces.

x(t) = ∑ k aj,k2−j/2φ(2−j t −k) +∑ k dj,k2−j/2ψ(2−j t −k) (2)

where φ(t) is scale function, ψ(t) is wave function, aj,k and dj,k are low and high frequency component of the decomposed signal x(t) given byEq. 3, Eq. 4

aj,k = ∑ m h(m−2k)aj−1,m (3)

dj,k = ∑ m g(m−2k)dj−1,m (4)

Where h(m) is the low-pass filter, g(m) is the high-pass filter, aj,k is the approximate coefficient, which represents the low frequency component of the signal, and dj,k is the detailed coefficient, which corresponds to the high-frequency component. The approximate coefficients at wavelet decomposition stage j are received via convolving the approximate coefficients at the earlier decomposition stage (j-1) with the low-cross filter coefficients. In a similar way, the specific coefficients at wavelet decomposition stage j are got by way of convolving the approximate coefficients on the previous decomposition stage (j-1) with the excessive-go filter coefficients. Such a approach represents the inspiration of Mallats algorithm to put in force the DWT, the place a sign is decomposed by means of a 2-level DWT. In the wake of going through the high-pass and low-pass channels on the primary (level 1), the yield of the low-pass channel, meant as the estimated coefficients and the output of the high-pass filter, denoted as the detailed coefficients of the level 1, is filtered again by the second-level filter banks. Toward the finish of the second level disintegration, the flag is decayed into four element bunches: one gathering containing the least recurrence parts, meant as the approximate information and labelled as LL, and three groups containing progressively higher frequency components, called the detailed information and labelled as HH, HL and LH. Fig. 1 shows that HH sub-band contains only detailed coefficients in it. The group of the Daubechies wavelets is orthogonal, however awry, which presents a vast stage bending. This means that it cannot be used in applications where a signals phase information needs to be kept. It is usually a compactly supported base wavelet with a given support width of 2N −1, in which N is the order of the base wavelet. In theory, N can also be up to infinity. In real-world applications, the Daubechies wavelets with order up to 20 were used. The Daubechies wavelets shouldn't have specific expression besides for the one with N = 1, which is actually the Haar wavelet. With an increase of the support width (i.e., a develop of the base wavelet order), the Daubechies wavelet becomes more and more smoother, leading to raised frequency localization.

 

Advantages of DWT:

1) It gives info about both frequency and time of the signal, transform of a non-stationary sign is efficiently acquired, without  loss of much resolution, decreases the size of image,

2) Also decreases the redundancy along with computational time.

• Discrete Fourier Transform:

DFT transforms an image from spatial domain to frequency domain. Using DFT the magnitude and phase of an image in frequency domain can be separated, which is one of the advantages of DFT utilized in DIW. Also DFT has properties such as shift variance, less interpolation error and scaling which help in better recovery of image under attacks. The 2D-DFT of an image f(x,y) of dimension M ×N is given by Eq. 5

F (u, v) = 1 MN M−1 ∑ x=0 N−1 ∑ y=0 f(x, y) e−j2π ( ux M + vy N ) (5)

The 2D-IDFT of f(u,v) is given by Eq. 6

F(x, y) = M−1 ∑ u=0 N−1 ∑ v=0 f(u,v)ej2π( ux M + vy N ) (6)

• Fast Fourier Transform:

To obtain FFT Coefficient, It is processed out on spatial domain image. [15, 16,17] magnitude value, phase angle, real and imaginary part are the features retrieved by FFT coefficients. When compared with DFT, FFT computation is much faster, only (N/2)[log2N] in FFT is against W while to compute N-point DFT, the required no of multiplication is the reason behind its slow process. To extract the features of DWT approximation band is required while Magnitude values are required for the features of FFT. To design the proposed algorithm, firstly we have introduced a Hybrid 3-Level DWT-FFT technique on both cover as well as WI. Further, we compute the singular values (SV) of LL sub band of both images then embedded these two images to obtain WI. Later on, inverse scheme is applied to obtain extracted WI.

FFT algorithm computes the DFT of a sequence, or their inverse. Fourier examination turns a signal from their distinctive domain to a representation in the volume domain and vice versa. An FFT computes the DFT and produces exactly the same impact as examining the DFT classification straight away; probably the most essential huge difference is that the FFT is much faster. (In the present presence of round-off problem, many FFT formulations will also be much more specific than examining the DFT classification straight away.[18]

 

• Singular Value Decomposition (SVD):

In most cases SVD is a numerical process which is used for diagonalizable matrices in numerical analysis. In SVD transformation, a matrix can also be decomposed into 3 matrices multiplication which is linear algebra approach that decomposes a furnish matrix into three element matrices are left singular vectors, SVs and right singular vectors set. 

SVD WATERMARKING:

SVD of a image is given by Eq. 7

A = USVT (7)

Where U and V are orthogonal matrices and S is a diagonal matrix in which diagonal elements are arranged in the decreasing order. S matrix contains the SVs which are similar to increase control through which every corresponding input is multiplied to provide a corresponding result. The SVD use in DIW permit embedding of the watermark onto the singular frequency domain matrix or spatial domain coefficients of the image, watermark instead being directly embedded onto the image coefficients. The singular matrix is immune to modify because of various kind of attacks. Thus, the watermark can be extracted with very less loss of knowledge and protect transmission of information can be implemented.

 

SVD watermarking is estimate to work on the binary form. For an picture of N x N pixels and a binary p pixels watermark, separated the photograph into (N/4) x (N/4) non overlapping blocks whose measurement is 4X4 pixels .Which is based to the decide embedded blocks positions for every watermark bit. The segment are used in video watermarking are Inserting a watermark, it consists of a watermark insertion unit that uses usual video, watermark and a consumer key to found the watermarked video. Watermark addition unit, It comprises of the client key, input video and the watermark is gone through a watermark inclusion unit which brings about a watermarked video. Watermark Extraction Unit, It has two stages are finding the watermark and recuperating the watermark data. Watermark Detection Unit comprises of an extraction unit to first concentrate the watermark for contrasting it and the first watermark embedded and the yield is yes or no relying upon whether the watermark is available.[19].

• Gaussian Filter:

It is an ideal time domain filter Gaussian function as its impulse response which is used in noise suppression. The filter smoothes out the noise in the image. But too much smoothing can result in the loss of the edge feature of the image. The Gaussian filter (GF) modifies the enter sign via convolution with a Gaussian function. A two-dimensional GF is given with the aid of Eq. Eight

 

G(x, y) =1/√2πσe−(x2+y2)2σ2 (8)

 

Where σ is the standard deviation.

 

 

CHAPTER 2

LITERATURE REVIEW

 

Lots of work towards the area of DW have been done in past. This section presents a brief survey of existing approaches to embed the watermark and extract the cover image (CI) successfully with their advantages and limitations.

Shaikh et.al [21] in this paper video watermarking with 3-level DWT is proposed which is perceptually invisible. In proposed work using two different videos and different logo images and shown how watermark is detected and watermarks not detected. The secret key's given to WI in the course of embedding method and at the same time extracting the WI the identical secret key is used. The result of MSE should be as low as possible to have less error and the PSNR should be as high as possible to have better quality of reconstructed video.

Baiying et.al [22] in this paper proposed a robust audio watermarking scheme based on LWT-DCTSVD, DWT-DCTSVD with exploration of DE optimization and DM quantization. The appealing properties of SVD, LWT/DWT-DCT, and DE and quantization system make our scheme very mighty to quite a lot of usual signal processing attacks.  Meanwhile, the proposed scheme is not only robust against hybrid and resynchronizations attacks, but also robust against the Strimark for audio attacks. The experimental outcome validate that the proposed watermarking scheme has excellent imperceptibility too the comparison results with other SVD-based and similar algorithms indicate the superiority of scheme.

1. Shantikumar Singh, et.al. [23] in 2013, here they have reviewed some recent algorithms, proposed a classification based on their intrinsic features, inserting methods and extraction forms. Various watermarking algorithms are reviewed in the literatures which show benefits in programs utilizing WT with SVD. On this paper they also have provided an assessment of the large methods in existence for watermarking those that are employed in copyright protection. Along with these, an introduction to DW, residences of watermarking and its applications were awarded. In future works, the use of coding and cryptography watermarks will be approached.

Chen Li, et.al. [24] In “Wavelet Bases and Decomposition series in the DIW” consider and evaluate the presentation of exclusive wavelet bases within the DIW and the effect of different wavelet decomposition series for the DIW embedding based on the application of wavelet in the DIW. The experiments proved the DIW embedding based on biorthogonal wavelet better than others.

XiongShunqing, et.al. [25] in” A New DW Algorithm Based on NSCT and SVD” proposed an new algorithm of DW based on combining the Non Sub Sampled Contourlet Transform and SVD, they first applied the NSCT to the image and extract the low-frequency sub-band of image, and then decompose the low-frequency sub-band of image by SVD, finally embed the watermarking in the decomposed SV). The experiment results show that the new algorithm has good ability in standing up to geometric attacking, especially rotation attacks.

Ghobadi et al. [26] gave an algorithm which uses LSB method. It is a blind audio watermarking technique tamper detection and prevention technique. After calculating the mean of each column separately, it is embedded by using LSB method. And then reverse of the above process is performed. In the extraction methodology, the tamper detection is done to be if any tampering is done and if yes then the location of the respective tamper. BER and SNR have been evaluated to find the robustness of the algorithm and it has been concluded that DCT, FFT and DWT can be more robust than this fragile watermarking algorithm.

Lalitha et. al. [27] future a DWT-Arnold transform founded audio WA.  After sampling and partitioning the audio signal, DWT is applied on the original audio signal to produces details and approximation sub-bands. After applying Arnold transform along with DWT on the image, it is embedded into the transformed audio signal. After this, inverse DWT is applied to acquire watermarked audio frame.  In the extraction algorithm, the sampled and portioned audio signal is transformed using DWT to obtain the respective coefficients and then the watermark is extracted and Arnold transform is applied again to obtain the image. The subjective evaluation is done to know the Mean Opinion Score. This is done to check the impartibility of the watermarked audio signal. Then objective evaluation is done as well by calculating MSE, PSNR etc., after quite a lot of subjective and purpose scan, it has been concluded that this manner is rather mighty in opposition to many attacks and excellent of the audio is preserved as well.

Lalithaet. al.[28],in 2011, proposed an algorithm which uses DWT-SVD technique of audio-watermarking. The DCTSVD has been compared with DWT-SVD as well. The DWT-SVD algorithm proves to be more robust than DCTSVD algorithm.

Nikmehret.al.[29] proposed a method of audio watermarking which uses both DWT and DCT. The segments produced as a result of the segmentation of the original audio signal further are divided into two sections. The synchronization bits are embedded into DWT coefficients of the 1^st component whereas the watermark bits are embedded into the DCT coefficients of the 2nd part The DCT coefficients are produced after performing DWT on the second section. This algorithm shows a very good resistance to the attacks.

 

Deepa Mathew, et.al. [30] Regarding the SVD based image watermarking scheme. She claims that the output result of SVD is more secure and robust. On this system, D and U accessories are used for embedding the watermark. Unlike distinctive transforms which makes use of constant orthogonal bases, SVD makes use of non consistent orthogonal bases. The effect of SVD gives good accuracy, excellent robustness and just right imperceptibility in resolving rightful possession of WI.

 

1. Gupta, et.al. [31] A robust algorithm of DIW based on DWT is presented. Authors have used blind watermarking techniques. In targeted, DIW algorithms which can be situated on the DWT have been generally recognized to be extra customary than others.

 

ShikhaTripathi et.al. [32] Proposed a DWT based dual watermarking system wherein each blind and non-blind algorithm is used for the copyright security of the cover/host picture and the watermark respectively. They used the suggestion of implanted 2 watermarks into the CI by using genuinely embedded just one, to authenticate the supply picture and safeguard the watermark concurrently. Here the DWT coefficients of the essential watermark (brand) are modified making use of a further smaller secondary binary picture (signal) and the mid frequency coefficients of the cover/host image.

 

SuraRamzi Sheriff, et.al. [33] An applied idea of linear algebra referred to as “Singular Value Decomposition (SVD)” is efficaciously applicable to DIW scheme, which embeds the watermark into an picture in an indiscernible manner. This method has been proposed an optimal SVD based watermarking scheme that embeds the watermark in two steps in the first step, the CI is split into smaller blocks and a bit of the watermark is embedded in each and every block.  In the second step, the watermark has extracted from the WI.

 

1. Bai et. al. [34] proposed a methodology in which an area of the entire audio signal was selected for watermark embedding and is designated by adaptive adjustment. This area was segmented, FFT was applied to every segment and finally watermark was embedded into dc coefficients (has good HAS properties). Synchronization code was introduced to confirm the initial position of the watermark. A Watermark was scrambled using Arnold transform to increase robustness. BER and NC were found to be 0 and 1 respectively, for attacks of requantization, resampling, additive noise, LP filtering, echo and for mp3 compression (except at 64, 48, and 32 kbps). Experimental results with SNR, listening test on 5 persons, BER and NC revealed that the watermarked audio signal is imperceptible and robust against various attacks.

 

1. Jiang et.al. [35]. DWT transform was applied to every frame of audio. Chaos encrypted watermark was embedded in the descending dimension in the SVs obtained after applying SVD decomposition to the approximate coefficient standards of wavelets. SNR Results demonstrate that the algorithm was fragile and could be used for checking the authenticity and integrity of information; however this algorithm cannot remove temper. SDG grades were found to be zero, which validates watermarked audio inaudibility.

 

Work done by Nikita Kashyap et al. [36] puts forward a technique for image watermarking that is based on a 2 level DWT. On this method the invisible watermark can also be embedded into salient points of the picture using alpha blending manner. This process distinguishes between the higher values of PSNR using second level DWT compared with single level DWT and indicates that 2-level DWT furnish higher show than 1-level DWT.

Chih-Chin Lai and Cheng-Chih Tsai [37] proposed a DIW manner to meet each robustness and imperceptibility necessities.  To achieve the target authors proposed a DIW scheme based on hybrid DWT and SVD. The proposed approach is not embedded watermark straight on the wavelet coefficients however alternatively than on the elements of SVs of the hidden pictures DWT sub-bands.

A novel technique for DIW by 2 levels DWT with SVD has been proposed in [38]. Initially, 2nd level DWT is performed on HL sub-band and then SVD is utilized to embed the watermark. Experimental results of the proposed procedure have proven the tremendous growth within the robustness underneath attacks. This process is robust for various attacks but some measures can be taken in future to increase the imperceptibility of the image.

A new robust watermarking technique, where in first of all, main image is converted by using the DWT as much as the three-layers, means observe three times, so that picture is divide into the special sub band (LL, LH, and HL, HH) and WI is embedded into the intermediate frequency sub band have been proposed with the aid of Qing Liu et al [39]. Spread spectrum science can also be taken for this procedure and blind watermarking procedure is applied to extract the watermark. Spread spectrum technology provides secure communications because signal is “hidden” like noise but the downside of this is, it increases bandwidth of signal and increases the complexity.

Malika Narang and Sharda Vashisth [40] proposed a DW technique in which paper watermarking is done by dwt which is Multi resolution technique. This gives moderate PSNR value.

A new DW process established on DET and SVD proposed in [41]. On this procedure watermark is embedded in immoderate-frequency band by way of SVD. The outcome display that the algorithm is able to with stand variety of attacks including common geometric attacks but the downside of this algorithm is its average Imperceptibility.

A new watermarking method founded on the DWT has been proposed by Xia and Boncelet et al. [42].  The authors introduce the decoding procedure of a probably marked image and are using DWT to do so. If the cross-correlation used to be above a threshold, then the watermark was once discovered. Otherwise else, the picture used to be once decomposed into finer and finer bands apart from the entire, extracted watermark was once correlated with the entire, fashioned watermark  This technique proved to be more robust than the DCT method.

A hybrid plan founded on DWT and SVD presented in [43]. After decomposing the hidden picture into 4 bands, SVD is applied to each and every band, after which identical watermark information is embedded by means of editing The SV. Because of the trade in all frequencies, makes it viable for the progress of a watermarking scheme that is amazing to a variety of attacks.

Authors in [44] have Matched watermarking making use of DWT & DWT-DCT ways efficiency analysis on the groundwork of PSNR and Similarity element of watermark and recovered watermark. The result of this technique shows that mixture of DWT-DCT method is the best technique for level one watermark embedding.

Neha Narula (2015) et al presents that the DW is an application related to the copyright security. Any digital object can be used as a carrier to carry knowledge. If the knowledge relates to object, then it is well-known as a watermark which can be invisible or visible. In the era of digital knowledge, there are the multiple risk regions like copyright and integrity violation of digital objects. In this paper, a comparative study of two more recent DW techniques, namely DWT and DWT-SVD over RGB images is presented. In case of DWT watermarking manner, decomposition of the original picture is completed to embed the watermark and in case of DWT-SVD watermarking procedure, to begin with usual picture is decomposed in line with DWT after which a watermark is embedded in SV got by means of making use of SVD. The performances of the proposed techniques are compared on the basis of PSNR values. [45]

Ms. Roshan Jahan (2013) et al reward that on this paper a new DI security scheme is utilized, which comprises watermarking algorithms utilizing DWT-SVD and Optimized chaotic founded picture encryption got by means of genetic algorithm (GA) with high level of robustness and protection. In this proposed scheme first of all the WI has been encrypted using the first-rate hybrid model for picture encryption composed of GA and chaotic function. After that the encrypted picture is embedded into the usual picture to type the WI. In the 1^st stage of the proposed encryption algorithm encrypted pictures are developed utilizing secret key and chaotic function. In the subsequent stage, these encrypted images are used as initial population for GA. In this paper, a primary time GA has been applied to the WP for encryption. The identical coefficient NC, PNSR and correlation coefficient (CR) are used to evaluate the transparency, robustness and safety of the algorithm. All the Experiment has been performed in MATLAB and outcomes are provided to illustrate that the proposed approach is providing good results. [46]

 

Pooja Malhotra (2013) et al present that Robustness geometric against the distortion is one of the crucial most important subjects in watermarking. On this paper, a novel DWT-SVD picture watermarking algorithm that is potent against usual इमेज प्रोसेसिंग (IP) and affine transformation is presented. That is in contrast to classical standpoint that assumes watermarking must be embedded in middle or low frequency to have just right robustness. PSNR and usual cross Correlation are computed to measure picture exceptional and template similar In addition, the competency of the suggested system is confirmed under general IP operations and a comparative study is made against our past method [47]

 

Sumit Kumar Prajapati (2012) et al present that with the rapid growth of the Internet and the development of digital multimedia technologies, illegal copying, Tampering, enhancing and copyright security has come to be very major troubles. As a consequence, there is a strong need of establishing the techniques to face all these issues. DW appeared as an answer for defending the multimedia data. On this paper, the endorse a process of nonblind change into domain watermarking founded on DWT- DCT- SVD. The parameters used to scan the robustness of the proposed algorithm are the PSNR and Weighted (PSNR) and correlation coefficient (ρ).Additionally, Robustness of the proposed algorithm are tested for more than a few attacks together with SPN and Gaussian noise, salt & pepper, sharpened and contrast adjustment. The experimental outcome exhibit that the proposed system is extra amazing towards one of kind kinds of attacks and the WI has good transparency. [48]

Seema (2012) et al present that In order to improve the robustness and imperceptibleness of the algorithm, a novel embedding and extracting method with DWT- SVD is proposed. The approximation matrix of the 3^rd level of picture in DWT domain is modified with SVD to embed the SV of the watermark to the SV of DWT coefficient. The suggested extracting and embedding technique was employed to accelerate the hybrid DWT - SVD watermarking and to avoid the lack of watermark. The experimental outcomes describe both the good robustness under various attacks and the high fidelity. The time needed to achieve the program is greatly decreased. [49]

It has been found that, research have been carried out with many Watermarking algorithms (spatial and transform domain) for embedding the WI in the cover image in IP. The main focus of each algorithm is to increase the imperceptibility and robustness of the WI. Each Algorithm is giving different results with the same parameters like PSNR, MSE etc

 

 

 

CHAPTER 3

PROPOSED METHODOLOGY

From literature review it has been observed that most of the approaches introduced in past having problems like Low Imperceptibility, Data embedding capacity is less, Quality and More conceptual complexity. These problems have been removed in proposed work.

    A new DW approach based on hybrid DWT_FFT and SVD have been proposed in this work. The proposed algorithm is developed based on three stages. Firstly different types of wavelets (such as haar, Bior 5.5, Bior 1.1, Sym 8 and coif 5) are applied on the host image to calculate the four sub-bands of original gray scale image. After that FFT is applied on to the LL sub band of host image. Later on SVD is calculated on LL sub band. To manage as well as to develop the force of the watermark, we have taken a scale factor.  At second stage watermarked image is retrieved by embedding SV of LL sub-band of both original gray scale image and WI. At the final stage of the algorithm exactly reverse practice is involved to remove WI from the WI. The performance of this scheme was estimated with respect to the imperceptibility. It can be seen from the results that the PSNR value of our proposed algorithm is higher. The proposed system provided good imperceptibility and the robustness.

 

• Proposed Algorithm

1.     Digital Watermarking

Step 1 Take the original image (a) and convert it into gray scale image using function-

I=rgb2gray (a)

Step 2 Now apply different types of wavelet transforms to “I” and decompose it into 4 sub bands LLз, LHз, HLз and HHз.

Step 3 Apply FFT onto the LL sub band of I.

F=fft2 (LLз)

Step 4 Apply SVD to the LL sub band i.e.

ILL= UI SI VIᵀ

Step 5 Now take the WI  and apply three levels DWT to decompose it into four sub bands i.e. LLз, LHз, HLз and HHз.

Step 6 Apply FFT onto the LL sub band of I.

F=fft2 (LLз)

Step 7 Apply SVD to the LL sub band of watermark i.e.

WLL =UW SW VWᵀ

Step 8 Modify the singular values (I_s) of Ie with the singular values (W_s) of watermark i.e.

S_n= I_s + α * W_s

Here α stands for scale factor.

Step 9 Now obtain modified DWT coefficient i.e.

IeLL’= U *S_n* V ᵀ

Step 10 At last, the watermarked image “W*” is obtained by applying inverse 3 level DWT.

 

2.      Watermark Extraction

Step 1 take the WI and apply the same process to calculate the SVs of WI.

Step 2 Subtract the SV of WI i.e. (Wm_s) from SV of original image i.e. (I_s) to get the SV of WI i.e.

S_w = (I_s - Wm_s)/α

Step 3 Obtain modified DWT coefficient i.e.

W*LL’ = U_ w * S_w* V_wᵀ

Step 4 Get the watermark image by applying inverse DWT_FFT process.

• Flow Chat for Proposed Algorithm

1.     Watermark Embedding

2.     Watermark Extraction

• Result Analysis

The proposed technique uses mixture of hybrid DWT_FFT along with SVD for embedding the watermark on the CI. The focus of DW in transform domain is to insert the max possible watermark signal without perceptually affecting image quality, so that the watermark must remain present as imperceptible and robust. There are a no. of watermarking way exists in transform domain. With the help of these techniques issues such as visual quality of the image and robustness can be accommodated, a single transform based watermarking is not able to satisfy diverse criteria desired for watermarking. The requirements such as imperceptibility with respect to payload capacity and robustness of watermarking system contradict each other. In order to increase the robustness, the payload should be increased but it decreases the imperceptibility of the image. The incorporation of imperceptibility and robustness simultaneously in watermarking system design is an issue that needs to be addressed. DWT reduces the image data and then watermark is embedded in high frequency sub bands. This will filtered out the unwanted information from the image. For this reason whilst to maintain the robustness and imperceptibility of the WI.

 

In figure1 we have shown that (a) is the gray scale host image, (b) shows the image retrieved after applying DWT transform  of host image. Then after applying FFT, SV of host Image is calculated. Than (c) shows the WI, (d) is the DWT transform of WI. After that similar procedure is applied to calculate the SV of WI. Than embedded the host and the watermark image using their singular values and scale factor Alfa i.e. (e). Than repeat the inverse of the process to extract the watermark image i.e. (f)

Table1 Shows the PSNR value of extracted watermark image. PSNR is a ratio most likely applied as the great measurement between the original and the compressed picture.  The more PSNR, the better quality of reconstructed or compressed image. The results of proposed algorithm gives the more PSNR values so the better quality of image.

     Table2. Shows MSE Values of Base & Propose Algorithm. For the realistic purposes, mean square Error, MSE makes it possible for researchers to examine the “true” pixel values of customary data with the degraded picture. As understood via the identify, MSE represents the usual of squares of the “errors” between the genuine picture and the noisy snapshot. The error can be calculated as the amount by which the values of the original image differ from the degraded image. Minimum value of MSE leads to the higher the quality of image.

The idea is that the better the PSNR, the easier degraded picture has been   reconstructed to compare the normal picture and the easier reconstructive algorithm. This would occur because we wish to minimize the MSE between images with respect the maximum signal value of the image.

 

Wavelet Name	Noise	Base PSNR	Propose PSNR
Haar	No	21.4123	50.0913
Wavelet Function	Salt & Pepper	21.2431	34.0682
Bior 5.5	No	21.4221	50.4864
	Salt & Pepper	21.2229	34.0624
Bior 1.1	No	21.4123	50.0913
	Salt & Pepper	21.2431	34.0682
Sym8	No	21.4120	49.7629
	Salt & Pepper	21.2430	34.0430
Coif5	No	21.4126	49.4620
	Salt & Pepper	21.2434	34.0349

 

 

 

 

 

	Table.1. Shows PSNR value of Base and Propose Method with and without Noise Attack

 

 

 

 

 

 

Wavelet Function	Base MSE	Propose MSE
Haar	0.0072	0.0031
Bior 5.5	0.0070	0.0030
Bior 1.1	0.0072	0.0031
Sym8	0.0071	0.0032
Coif5	0.0075	0.0034

 

Table.1. Shows MSE value of Base and Propose Method without Noise Attack

 

 

• Screen Shots

Step 1: Select Cover Image

Step 2: Select Watermark Image

Case 3: Watermarking Using Haar Transform

Embed watermark

Extract Watermark

Performance Evaluation

Noise Attack

Case 4: Watermarking Using Bior 1.1 Transform

Embed Watermark

Extraction Watermark

Performance Evaluation

Noise Attack

Case 5: Watermarking Using Bior 1.1 Transform

Embed Watermark

Extract Watermark

Performance Evaluation

Noise Attack

Case 6: Watermarking Using Sym 8 transform

Embed Watermark

Extract Watermark

Performance Evluation

Noise Attack

Case 7: Watermarking  using Coif 5 transform

Embed Watermark

Extract Watermark

Performance Evaluation

Noise Attack

 

 

 

 

 

Conclusion

The proposed way uses the hybrid DWT-FFT technique along with SVD technique for embedding the watermark on the CI and follows the reverse scheme to extract the WI from WI. The proposed work is able to achieve moderate robustness, high imperceptibility with reduced amount of data to be processed. A no. of experiments have been taken and Analysis is done based on experimental results which shows improved performance of the proposed method when compared with the Single stage DWT-SVD founded watermarking introduced in Base process. PSNR value generated from proposed algorithm is much higher than base algorithm as well as MSE value is much Lower which assures the better quality of images. The combination of three techniques hybrid DWT-FFT along with SVD, introduced in proposed method is the reason behind the better performance, good imperceptibility and enhanced quality of image.

 

 

 

 

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