Hybrid multiple watermarking technique for securing medical images of modalities MRI, CT scan, and X-ray

In order to contribute to the security of sharing and transferring medical images, we had presented a multiple watermarking technique for multiple protections; it was based on the combination of three transformations: the discrete wavelet transform (DWT), the fast Walsh-Hadamard transform (FWHT) and, the singular value decomposition (SVD). In this paper, three watermark images of sizes 512x 512 were inserted into a single medical image of various modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and X-Radiation (X-ray). After applying DWT up to the third level on the original image, the high-resolution sub-bands were being selected subsequently to apply FWHT and then SVD. The singular values of the three watermark images were inserted into the singular values of the cover medical image. The experimental results showed the effectiveness of the proposed method in terms of quality and robustness compared to other reported techniques cited in the literature

Modified DCT-based Audio Watermarking Optimization using Genetics Algorithm

Ease process digital data information exchange impact on the increase in cases of copyright infringement. Audio watermarking is one solution in providing protection for the owner of the work. This research aims to optimize the insertion parameters on Modified Discrete Cosine Transform (M-DCT) based audio watermarking using a genetic algorithm, to produce better audio resistance. MDCT is applied after reading host audio, then embedding in MDCT domain is applied by Quantization Index Modulation (QIM) technique. Insertion within the MDCT domain is capable of generating a high imperceptible watermarked audio due to its overlapping frame system. The system is optimized using genetic algorithms to improve the value of imperceptibility and robustness in audio watermarking. In this research, the average SNR reaches 20 dB, and ODG reaches -0.062. The subjective quality testing on the system obtains an average MOS of 4.22 out of five songs tested. In addition, the system is able to withstand several attacks. The use of M-DCT in audio watermaking is capable of producing excellent imperceptibility and better watermark robustness

ANALISIS OPTIMASI DENGAN ALGORITMA GENETIKA PADA AUDIO WATERMARKING BERBASIS DISCRETE WAVELET TRANSFORM

Perkembangan teknologi informasi terutama pada internet dan multimedia, pengiriman dan penyebaran media digital menjadi lebih mudah dilakukan. Hal ini menyebabkan seringnya terjadi pelanggaran hak cipta, perti mengambil dan memodifikasi data multimedia tersebut secara ilegal. Untuk mengurangi hal tersebut, maka watermarking dibutuhkan. Teknik watermarking berguna untuk menyembunyikan atau penanaman data/informasi tertentu ke dalam suatu data digital lainya. Cara penyisipan informasi kedalam data digital dilakukan sedemikian rupa sehingga tidak terasa keberadaannya dan dapat diekstrak kembali dengan benar. Sistem watermarking pada file audio yang biasa digunakan sebagai perlindungan hak cipta. Sistem ini mempunyai tujuan utama untuk menyisipkan sebuah data data berupa bit kedalam file audio berformat .*wav dimana pesan akan diubah terlebih dahulu ke bentuk biner. Sistem Watermarking yang akan dibangun menggunakan metode Discrete Wavelet Transform (DWT) untuk proses embbeding dan extracting akan selanjutnya di analisis mengunakan proses algoritma genetika dan untuk penyisipannya mengunakan metode rata - rata. Sebuah sistem watermarking pada file audio. Parameter yang akan di optimasikan oleh Algoritma genetika adalah alpha, subband, dan level, parameter tersebut berguna sebagai parameter pengisipan yang terbaik. Dimana untuk proses pengujian kualitas audio menggunakan parameter PEAQ (Perceptual Evaluation of Audio Quality), MOS (Mean Opinion Score), dan BER (Bit Error Rate) diharapkan diperoleh hasil baik dimana rata - rata PEAQ > -1 dan rata - rata MOS > 4 dan untuk pengujian kualitas pesan menggunakan parameter BER sistem mampu menghasilkan nilai BER < 5%. Kata Kunci : Watermarking, Discrete Wavelet Transform, Algoritma Genetik

Customizing Fuzzy Partitions for Visual Texture Representation

Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like "very coarse", "low directional", or "high contrasted". Thus, computational models with the ability of providing a perceptual texture characterization on the basis of these terms play a fundamental role in tasks where some interaction with subjects is needed. In this sense, fuzzy partitions defined on the domain of computational measures of the corresponding property have been proposed in the literature. However, the main drawback of these proposals is that they do not take into account the subjectivity associated to the human perception. For example, the perception of a texture property may change depending on the user, and in addition, the image context may influence the global perception of the properties. In this paper, we propose to solve these problems by means of a methodology that automatically adapts any generic fuzzy partition modeling a texture property to the particular perception of a user or to the image context. In this method, the membership functions associated to the fuzzy sets are automatically adapted by means of a functional transformation on the basis of the new perception. For this purpose, the information given by the user or extracted from the textures present in the image are employed.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund - ERDF (Fondo Europeo de Desarrollo Regional - FEDER) under project TIN2014-58227-P Descripción lingüística de información visual mediante técnicas de minería de datos y computación flexible

New watermarking methods for digital images.

The phenomenal spread of the Internet places an enormous demand on content-ownership-validation. In this thesis, four new image-watermarking methods are presented. One method is based on discrete-wavelet-transformation (DWT) only while the rest are based on DWT and singular-value-decomposition (SVD) ensemble. The main target for this thesis is to reach a new blind-watermarking-method. Method IV presents such watermark using QR-codes. The use of QR-codes in watermarking is novel. The choice of such application is based on the fact that QR-Codes have errors self-correction-capability of 5% or higher which satisfies the nature of digital-image-processing. Results show that the proposed-methods introduced minimal distortion to the watermarked images as compared to other methods and are robust against JPEG, resizing and other attacks. Moreover, watermarking-method-II provides a solution to the detection of false watermark in the literature. Finally, method IV presents a new QR-code guided watermarking-approach that can be used as a steganography as well. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b183575

Intelligent watermarking of long streams of document images

Digital watermarking has numerous applications in the imaging domain, including (but not limited to) fingerprinting, authentication, tampering detection. Because of the trade-off between watermark robustness and image quality, the heuristic parameters associated with digital watermarking systems need to be optimized. A common strategy to tackle this optimization problem formulation of digital watermarking, known as intelligent watermarking (IW), is to employ evolutionary computing (EC) to optimize these parameters for each image, with a computational cost that is infeasible for practical applications. However, in industrial applications involving streams of document images, one can expect instances of problems to reappear over time. Therefore, computational cost can be saved by preserving the knowledge of previous optimization problems in a separate archive (memory) and employing that memory to speedup or even replace optimization for future similar problems. That is the basic principle behind the research presented in this thesis. Although similarity in the image space can lead to similarity in the problem space, there is no guarantee of that and for this reason, knowledge about the image space should not be employed whatsoever. Therefore, in this research, strategies to appropriately represent, compare, store and sample from problem instances are investigated. The objective behind these strategies is to allow for a comprehensive representation of a stream of optimization problems in a way to avoid re-optimization whenever a previously seen problem provides solutions as good as those that would be obtained by reoptimization, but at a fraction of its cost. Another objective is to provide IW systems with a predictive capability which allows replacing costly fitness evaluations with cheaper regression models whenever re-optimization cannot be avoided. To this end, IW of streams of document images is first formulated as the problem of optimizing a stream of recurring problems and a Dynamic Particle Swarm Optimization (DPSO) technique is proposed to tackle this problem. This technique is based on a two-tiered memory of static solutions. Memory solutions are re-evaluated for every new image and then, the re-evaluated fitness distribution is compared with stored fitness distribution as a mean of measuring the similarity between both problem instances (change detection). In simulations involving homogeneous streams of bi-tonal document images, the proposed approach resulted in a decrease of 95% in computational burden with little impact in watermarking performace. Optimization cost was severely decreased by replacing re-optimizations with recall to previously seen solutions. After that, the problem of representing the stream of optimization problems in a compact manner is addressed. With that, new optimization concepts can be incorporated into previously learned concepts in an incremental fashion. The proposed strategy to tackle this problem is based on Gaussian Mixture Models (GMM) representation, trained with parameter and fitness data of all intermediate (candidate) solutions of a given problem instance. GMM sampling replaces selection of individual memory solutions during change detection. Simulation results demonstrate that such memory of GMMs is more adaptive and can thus, better tackle the optimization of embedding parameters for heterogeneous streams of document images when compared to the approach based on memory of static solutions. Finally, the knowledge provided by the memory of GMMs is employed as a manner of decreasing the computational cost of re-optimization. To this end, GMM is employed in regression mode during re-optimization, replacing part of the costly fitness evaluations in a strategy known as surrogate-based optimization. Optimization is split in two levels, where the first one relies primarily on regression while the second one relies primarily on exact fitness values and provide a safeguard to the whole system. Simulation results demonstrate that the use of surrogates allows for better adaptation in situations involving significant variations in problem representation as when the set of attacks employed in the fitness function changes. In general lines, the intelligent watermarking system proposed in this thesis is well adapted for the optimization of streams of recurring optimization problems. The quality of the resulting solutions for both, homogeneous and heterogeneous image streams is comparable to that obtained through full optimization but for a fraction of its computational cost. More specifically, the number of fitness evaluations is 97% smaller than that of full optimization for homogeneous streams and 95% for highly heterogeneous streams of document images. The proposed method is general and can be easily adapted to other applications involving streams of recurring problems

Exploiting similarities between secret and cover images for improved embedding efficiency and security in digital steganography

The rapid advancements in digital communication technology and huge increase in computer power have generated an exponential growth in the use of the Internet for various commercial, governmental and social interactions that involve transmission of a variety of complex data and multimedia objects. Securing the content of sensitive as well as personal transactions over open networks while ensuring the privacy of information has become essential but increasingly challenging. Therefore, information and multimedia security research area attracts more and more interest, and its scope of applications expands significantly. Communication security mechanisms have been investigated and developed to protect information privacy with Encryption and Steganography providing the two most obvious solutions. Encrypting a secret message transforms it to a noise-like data which is observable but meaningless, while Steganography conceals the very existence of secret information by hiding in mundane communication that does not attract unwelcome snooping. Digital steganography is concerned with using images, videos and audio signals as cover objects for hiding secret bit-streams. Suitability of media files for such purposes is due to the high degree of redundancy as well as being the most widely exchanged digital data. Over the last two decades, there has been a plethora of research that aim to develop new hiding schemes to overcome the variety of challenges relating to imperceptibility of the hidden secrets, payload capacity, efficiency of embedding and robustness against steganalysis attacks. Most existing techniques treat secrets as random bit-streams even when dealing with non-random signals such as images that may add to the toughness of the challenges.This thesis is devoted to investigate and develop steganography schemes for embedding secret images in image files. While many existing schemes have been developed to perform well with respect to one or more of the above objectives, we aim to achieve optimal performance in terms of all these objectives. We shall only be concerned with embedding secret images in the spatial domain of cover images. The main difficulty in addressing the different challenges stems from the fact that the act of embedding results in changing cover image pixel values that cannot be avoided, although these changes may not be easy to detect by the human eye. These pixel changes is a consequence of dissimilarity between the cover LSB plane and the secretimage bit-stream, and result in changes to the statistical parameters of stego-image bit-planes as well as to local image features. Steganalysis tools exploit these effects to model targeted as well as blind attacks. These challenges are usually dealt with by randomising the changes to the LSB, using different/multiple bit-planes to embed one or more secret bits using elaborate schemes, or embedding in certain regions that are noise-tolerant. Our innovative approach to deal with these challenges is first to develop some image procedures and models that result in increasing similarity between the cover image LSB plane and the secret image bit-stream. This will be achieved in two novel steps involving manipulation of both the secret image and the cover image, prior to embedding, that result a higher 0:1 ratio in both the secret bit-stream and the cover pixels‘ LSB plane. For the secret images, we exploit the fact that image pixel values are in general neither uniformly distributed, as is the case of random secrets, nor spatially stationary. We shall develop three secret image pre-processing algorithms to transform the secret image bit-stream for increased 0:1 ratio. Two of these are similar, but one in the spatial domain and the other in the Wavelet domain. In both cases, the most frequent pixels are mapped onto bytes with more 0s. The third method, process blocks by subtracting their means from their pixel values and hence reducing the require number of bits to represent these blocks. In other words, this third algorithm also reduces the length of the secret image bit-stream without loss of information. We shall demonstrate that these algorithms yield a significant increase in the secret image bit-stream 0:1 ratio, the one that based on the Wavelet domain is the best-performing with 80% ratio.For the cover images, we exploit the fact that pixel value decomposition schemes, based on Fibonacci or other defining sequences that differ from the usual binary scheme, expand the number of bit-planes and thereby may help increase the 0:1 ratio in cover image LSB plane. We investigate some such existing techniques and demonstrate that these schemes indeed lead to increased 0:1 ratio in the corresponding cover image LSB plane. We also develop a new extension of the binary decomposition scheme that is the best-performing one with 77% ratio. We exploit the above two steps strategy to propose a bit-plane(s) mapping embedding technique, instead of bit-plane(s) replacement to make each cover pixel usable for secret embedding. This is motivated by the observation that non-binary pixel decomposition schemes also result in decreasing the number of possible patterns for the three first bit-planes to 4 or 5 instead of 8. We shall demonstrate that the combination of the mapping-based embedding scheme and the two steps strategy produces stego-images that have minimal distortion, i.e. reducing the number of the cover pixels changes after message embedding and increasing embedding efficiency. We shall also demonstrate that these schemes result in reasonable stego-image quality and are robust against all the targeted steganalysis tools but not against the blind SRM tool. We shall finally identify possible future work to achieve robustness against SRM at some payload rates and further improve stego-image quality

Applications of MATLAB in Science and Engineering

The book consists of 24 chapters illustrating a wide range of areas where MATLAB tools are applied. These areas include mathematics, physics, chemistry and chemical engineering, mechanical engineering, biological (molecular biology) and medical sciences, communication and control systems, digital signal, image and video processing, system modeling and simulation. Many interesting problems have been included throughout the book, and its contents will be beneficial for students and professionals in wide areas of interest