Robust and Secure Watermarking Using Sparse Information of Watermark for Biometric Data Protection

Biometric based human authentication system is used for security purpose in many organizations in the present world. This biometric authentication system has several vulnerable points. Two of vulnerable points are protection of biometric templates at system database and protection of biometric templates at communication channel between two modules of biometric authentication systems. In this paper proposed a robust watermarking scheme using the sparse information of watermark biometric to secure vulnerable point like protection of biometric templates at the communication channel of biometric authentication systems. A compressive sensing theory procedure is used for generation of sparse information on watermark biometric data using detail wavelet coefficients. Then sparse information of watermark biometric data is embedded into DCT coefficients of host biometric data. This proposed scheme is robust to common signal processing and geometric attacks like JPEG compression, adding noise, filtering, and cropping, histogram equalization. This proposed scheme has more advantages and high quality measures compared to existing schemes in the literature

COMPRESSIVE SENSING BERBASIS DCT-DWT UNTUK KOMPRESI WATERMARK PADA WATERMARKING CITRA DIGITAL DENGAN DOMAIN SWT-SVD

Image watermarking adalah proses perlindungan hak cipta dari suatu objek (gambar) dengan memberikan tanda di dalam objek tersebut. Teknik image watermarking, dikatakan baik apabila tanda yang disisipkan tidak tampak oleh kasat mata dan objek pembawanya tidak mengalami penurunan kualitas serta tanda yang disisipkan harus tahan terhadap berbagai pengolahan sinyal. Pada tugas akhir ini di implementasikan sistem image watermarking pada watermark dan host dalam bentuk citra hitam putih dan Red Green Blue (RGB). Pada sistem image watermarking menggunakan metode Compressive Sensing (CS) berbasis Discrete Cosine Transform-Discrete Wavelet Transform (DCT-DWT), Stationary Wavelet Transform-Singular Value Decomposition (SWT-SVD) dan proses rekontruksi menggunakan algoritme Orthogonal Matching Pursuit (OMP). Pengujian dilakukan terhadap sistem yang di bangun dan telah dilakukan beberapa skenario pengujian dalam memperoleh analisis terkait dengan performansi sistem yang mampu dihasilkan. Pengaruh CS pada pengujian menghasilkan nilai Mean Square Error (MSE) 4,396208 , Peak Signal to Noise Ratio (PSNR) 41,70416 dan Bit Error Rate (BER) 0,185565. Dan pengaruh tanpa CS pada pengujian menghasilkan nilai Mean Square Error (MSE) 3,53102, Peak Signal to Noise Ratio (PSNR) 42,6518 dan Bit Error Rate (BER) 0,19686 Kata kunci: Image watermarking, CS, DCT-DWT, SWT-SVD, OMP, MSE, PSNR, BE

MULTIPLE DICTIONARY FOR SPARSE MODELING

Much of the progress made in image processing in the past decades can be attributed to better modeling of image content, and a wise deployment of these models in relevant applications. In this paper, we review the role of this recent model in image processing, its rationale, and models related to it. As it turns out, the field of image processing is one of the main beneficiaries from the recent progress made in the theory and practice of sparse and redundant representations. Sparse coding is a key principle that underlies wavelet representation of images. Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. In general, the choice of a proper dictionary can be done using one of two ways: i) building asparsifying  dictionary based on a mathematical model of the data, or ii) learning a dictionary to perform best on a training set

Overview of compressed sensing: Sensing model, reconstruction algorithm, and its applications

With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist-Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials. © 2020 by the authors

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

Hierarchical visual perception and two-dimensional compressive sensing for effective content-based color image retrieval

Content-based image retrieval (CBIR) has been an active research theme in the computer vision community for over two decades. While the field is relatively mature, significant research is still required in this area to develop solutions for practical applications. One reason that practical solutions have not yet been realized could be due to a limited understanding of the cognitive aspects of the human vision system. Inspired by three cognitive properties of human vision, namely, hierarchical structuring, color perception and embedded compressive sensing, a new CBIR approach is proposed. In the proposed approach, the Hue, Saturation and Value (HSV) color model and the Similar Gray Level Co-occurrence Matrix (SGLCM) texture descriptors are used to generate elementary features. These features then form a hierarchical representation of the data to which a two-dimensional compressive sensing (2D CS) feature mining algorithm is applied. Finally, a weighted feature matching method is used to perform image retrieval. We present a comprehensive set of results of applying our proposed Hierarchical Visual Perception Enabled 2D CS approach using publicly available datasets and demonstrate the efficacy of our techniques when compared with other recently published, state-of-the-art approaches

The dynamics of complex systems. Studies and applications in computer science and biology

Our research has focused on the study of complex dynamics and on their use in both information security and bioinformatics. Our first work has been on chaotic discrete dynamical systems, and links have been established between these dynamics on the one hand, and either random or complex behaviors. Applications on information security are on the pseudorandom numbers generation, hash functions, informationhiding, and on security aspects on wireless sensor networks. On the bioinformatics level, we have applied our studies of complex systems to theevolution of genomes and to protein folding

Toward sparse and geometry adapted video approximations

Video signals are sequences of natural images, where images are often modeled as piecewise-smooth signals. Hence, video can be seen as a 3D piecewise-smooth signal made of piecewise-smooth regions that move through time. Based on the piecewise-smooth model and on related theoretical work on rate-distortion performance of wavelet and oracle based coding schemes, one can better analyze the appropriate coding strategies that adaptive video codecs need to implement in order to be efficient. Efficient video representations for coding purposes require the use of adaptive signal decompositions able to capture appropriately the structure and redundancy appearing in video signals. Adaptivity needs to be such that it allows for proper modeling of signals in order to represent these with the lowest possible coding cost. Video is a very structured signal with high geometric content. This includes temporal geometry (normally represented by motion information) as well as spatial geometry. Clearly, most of past and present strategies used to represent video signals do not exploit properly its spatial geometry. Similarly to the case of images, a very interesting approach seems to be the decomposition of video using large over-complete libraries of basis functions able to represent salient geometric features of the signal. In the framework of video, these features should model 2D geometric video components as well as their temporal evolution, forming spatio-temporal 3D geometric primitives. Through this PhD dissertation, different aspects on the use of adaptivity in video representation are studied looking toward exploiting both aspects of video: its piecewise nature and the geometry. The first part of this work studies the use of localized temporal adaptivity in subband video coding. This is done considering two transformation schemes used for video coding: 3D wavelet representations and motion compensated temporal filtering. A theoretical R-D analysis as well as empirical results demonstrate how temporal adaptivity improves coding performance of moving edges in 3D transform (without motion compensation) based video coding. Adaptivity allows, at the same time, to equally exploit redundancy in non-moving video areas. The analogy between motion compensated video and 1D piecewise-smooth signals is studied as well. This motivates the introduction of local length adaptivity within frame-adaptive motion compensated lifted wavelet decompositions. This allows an optimal rate-distortion performance when video motion trajectories are shorter than the transformation "Group Of Pictures", or when efficient motion compensation can not be ensured. After studying temporal adaptivity, the second part of this thesis is dedicated to understand the fundamentals of how can temporal and spatial geometry be jointly exploited. This work builds on some previous results that considered the representation of spatial geometry in video (but not temporal, i.e, without motion). In order to obtain flexible and efficient (sparse) signal representations, using redundant dictionaries, the use of highly non-linear decomposition algorithms, like Matching Pursuit, is required. General signal representation using these techniques is still quite unexplored. For this reason, previous to the study of video representation, some aspects of non-linear decomposition algorithms and the efficient decomposition of images using Matching Pursuits and a geometric dictionary are investigated. A part of this investigation concerns the study on the influence of using a priori models within approximation non-linear algorithms. Dictionaries with a high internal coherence have some problems to obtain optimally sparse signal representations when used with Matching Pursuits. It is proved, theoretically and empirically, that inserting in this algorithm a priori models allows to improve the capacity to obtain sparse signal approximations, mainly when coherent dictionaries are used. Another point discussed in this preliminary study, on the use of Matching Pursuits, concerns the approach used in this work for the decompositions of video frames and images. The technique proposed in this thesis improves a previous work, where authors had to recur to sub-optimal Matching Pursuit strategies (using Genetic Algorithms), given the size of the functions library. In this work the use of full search strategies is made possible, at the same time that approximation efficiency is significantly improved and computational complexity is reduced. Finally, a priori based Matching Pursuit geometric decompositions are investigated for geometric video representations. Regularity constraints are taken into account to recover the temporal evolution of spatial geometric signal components. The results obtained for coding and multi-modal (audio-visual) signal analysis, clarify many unknowns and show to be promising, encouraging to prosecute research on the subject

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering