Rapid intelligent watermarking system for high-resolution grayscale facial images

Facial captures are widely used in many access control applications to authenticate individuals, and grant access to protected information and locations. For instance, in passport or smart card applications, facial images must be secured during the enrollment process, prior to exchange and storage. Digital watermarking may be used to assure integrity and authenticity of these facial images against unauthorized manipulations, through fragile and robust watermarking, respectively. It can also combine other biometric traits to be embedded as invisible watermarks in these facial captures to improve individual verification. Evolutionary Computation (EC) techniques have been proposed to optimize watermark embedding parameters in IntelligentWatermarking (IW) literature. The goal of such optimization problem is to find the trade-off between conflicting objectives of watermark quality and robustness. Securing streams of high-resolution biometric facial captures results in a large number of optimization problems of high dimension search space. For homogeneous image streams, the optimal solutions for one image block can be utilized for other image blocks having the same texture features. Therefore, the computational complexity for handling a stream of high-resolution facial captures is significantly reduced by recalling such solutions from an associative memory instead of re-optimizing the whole facial capture image. In this thesis, an associative memory is proposed to store the previously calculated solutions for different categories of texture using the optimization results of the whole image for few training facial images. A multi-hypothesis approach is adopted to store in the associative memory the solutions for different clustering resolutions (number of blocks clusters based on texture features), and finally select the optimal clustering resolution based on the watermarking metrics for each facial image during generalization. This approach was verified using streams of facial captures from PUT database (Kasinski et al., 2008). It was compared against a baseline system representing traditional IW methods with full optimization for all stream images. Both proposed and baseline systems are compared with respect to quality of solution produced and the computational complexity measured in fitness evaluations. The proposed approach resulted in a decrease of 95.5% in computational burden with little impact in watermarking performance for a stream of 198 facial images. The proposed framework Blockwise Multi-Resolution Clustering (BMRC) has been published in Machine Vision and Applications (Rabil et al., 2013a) Although the stream of high dimensionality optimization problems are replaced by few training optimizations, and then recalls from an associative memory storing the training artifacts. Optimization problems with high dimensionality search space are challenging, complex, and can reach up to dimensionality of 49k variables represented using 293k bits for high-resolution facial images. In this thesis, this large dimensionality problem is decomposed into smaller problems representing image blocks which resolves convergence problems with handling the larger problem. Local watermarking metrics are used in cooperative coevolution on block level to reach the overall solution. The elitism mechanism is modified such that the blocks of higher local watermarking metrics are fetched across all candidate solutions for each position, and concatenated together to form the elite candidate solutions. This proposed approach resulted in resolving premature convergence for traditional EC methods, and thus 17% improvement on the watermarking fitness is accomplished for facial images of resolution 2048×1536. This improved fitness is achieved using few iterations implying optimization speedup. The proposed algorithm Blockwise Coevolutionary Genetic Algorithm (BCGA) has been published in Expert Systems with Applications (Rabil et al., 2013c). The concepts and frameworks presented in this thesis can be generalized on any stream of optimization problems with large search space, where the candidate solutions consist of smaller granularity problems solutions that affect the overall solution. The challenge for applying this approach is finding the significant feature for this smaller granularity that affects the overall optimization problem. In this thesis the texture features of smaller granularity blocks represented in the candidate solutions are affecting the watermarking fitness optimization of the whole image. Also the local metrics of these smaller granularity problems are indicating the fitness produced for the larger problem. Another proposed application for this thesis is to embed offline signature features as invisible watermark embedded in facial captures in passports to be used for individual verification during border crossing. The offline signature is captured from forms signed at borders and verified against the embedded features. The individual verification relies on one physical biometric trait represented by facial captures and another behavioral trait represented by offline signature

A Brief Review of Cuckoo Search Algorithm (CSA) Research Progression from 2010 to 2013

Cuckoo Search Algorithm is a new swarm intelligence algorithm which based on breeding behavior of the Cuckoo bird. This paper gives a brief insight of the advancement of the Cuckoo Search Algorithm from 2010 to 2013. The first half of this paper presents the publication trend of Cuckoo Search Algorithm. The remaining of this paper briefly explains the contribution of the individual publication related to Cuckoo Search Algorithm. It is believed that this paper will greatly benefit the reader who needs a bird-eyes view of the Cuckoo Search Algorithm’s publications trend

A robust facemask forgery detection system in video

An in-depth fake video uses an Artificial Intelligent (AI), AI programming, and a Personal computer (PC) mix to create a deep fake video of the action. Deep-faking can also be used to represent images and sounds. We provide insights into our reviews in this document. We're showing our dataset to start. At this point, we present the subtleties and reproductively of exploratory settings to evaluate the discovered effects finally. It is no surprise to find deep fake videos, which only monitor a tiny section of the video (e.g., the target face appears quickly on the video; hence the time is limited). We remove our system's fixed duration's persistent effects as each video contributes to the preparation, approval, and testing sections to reflect this. The edge groups are isolated from each video successively (without outline skips). The entire pipeline is ready to be finished when the approval stage is ten years old. Convolutional Neural Network (CNN) was the best and most reliable of the classification systems. Fake videos typically use low-quality pictures to mask faults or insist that the general public regard camera defects as unexplainable phenomena. 'This is a common trope with Unidentified Flying Object (UFO) videos: ghostly orbs are lenses; snakes are compression artifacts on one's face. In this study, we have implemented a sophisticated, knowledgeable method to recognize false images. Our test results using various monitored videos have shown that we can reliably predict whether videos are monitored through with simple co-evolutionary Long Short-Term Memory (LSTM) structure

An Optimized Feature Selection Technique in Diversified Natural Scene Text for Classification Using Genetic Algorithm

Natural scene text classification is considered to be a challenging task because of diversified set of image contents, presence of degradations including noise, low contrast/resolution and the random appearance of foreground (font, style, sizes and orientations) and background properties. Above all, the high dimension of the input image's feature space is another major problem in such tasks. This work is aimed to tackle these problems and remove redundant and irrelevant features to improve the generalization properties of the classifier. In other words, the selection of a qualitative and discriminative set of features, aiming to reduce dimensionality that helps to achieve a successful pattern classification. In this work, we use a biologically inspired genetic algorithm because crossover employed in such algorithm significantly improve the quality of multimodal discriminative set of features and hence improve the classification accuracy for diversified natural scene text images. The Support Vector Machine (SVM) algorithm is used for classification and the average F-Score is used as fitness function and target condition. First after preprocessing input images, the whole feature space (population) is built using a multimodal feature representation technique. Second, a feature level fusion approach is used to combine the features. Third, to improve the average F-score of the classifier, we apply a meta-heuristic optimization technique using a GA for feature selection. The proposed algorithm is tested on five publically available datasets and the results are compared with various state-of-the-art methods. The obtained results proved that the proposed algorithm performs well while classifying textual and non-textual region with better accuracy than benchmark state-of-the-art algorithms.Qatar University [QUHI-CBE-21/22-1]

Breast cancer disease classification using fuzzy-ID3 algorithm based on association function

Breast cancer is the second leading cause of mortality among female cancer patients worldwide. Early detection of breast cancer is considerd as one of the most effective ways to prevent the disease from spreading and enable human can make correct decision on the next process. Automatic diagnostic methods were frequently used to conduct breast cancer diagnoses in order to increase the accuracy and speed of detection. The fuzzy-ID3 algorithm with association function implementation (FID3-AF) is proposed as a classification technique for breast cancer detection. The FID3-AF algorithm is a hybridisation of the fuzzy system, the iterative dichotomizer 3 (ID3) algorithm, and the association function. The fuzzy-neural dynamic-bottleneck-detection (FUZZYDBD) is considered as an automatic fuzzy database definition method, would aid in the development of the fuzzy database for the data fuzzification process in FID3-AF. The FID3-AF overcame ID3’s issue of being unable to handle continuous data. The association function is implemented to minimise overfitting and enhance generalisation ability. The results indicated that FID3-AF is robust in breast cancer classification. A thorough comparison of FID3-AF to numerous existing methods was conducted to validate the proposed method’s competency. This study established that the FID3-AF performed well and outperform other methods in breast cancer classification

A Field Guide to Genetic Programming

xiv, 233 p. : il. ; 23 cm.Libro ElectrónicoA Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authorsIntroduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation, Initialisation and GP 2.1 Representation 2.2 Initialising the Population 2.3 Selection 2.4 Recombination and Mutation Operators in Tree-based 3 Getting Ready to Run Genetic Programming 19 3.1 Step 1: Terminal Set 19 3.2 Step 2: Function Set 20 3.2.1 Closure 21 3.2.2 Sufficiency 23 3.2.3 Evolving Structures other than Programs 23 3.3 Step 3: Fitness Function 24 3.4 Step 4: GP Parameters 26 3.5 Step 5: Termination and solution designation 27 4 Example Genetic Programming Run 4.1 Preparatory Steps 29 4.2 Step-by-Step Sample Run 31 4.2.1 Initialisation 31 4.2.2 Fitness Evaluation Selection, Crossover and Mutation Termination and Solution Designation Advanced Genetic Programming 5 Alternative Initialisations and Operators in 5.1 Constructing the Initial Population 5.1.1 Uniform Initialisation 5.1.2 Initialisation may Affect Bloat 5.1.3 Seeding 5.2 GP Mutation 5.2.1 Is Mutation Necessary? 5.2.2 Mutation Cookbook 5.3 GP Crossover 5.4 Other Techniques 32 5.5 Tree-based GP 39 6 Modular, Grammatical and Developmental Tree-based GP 47 6.1 Evolving Modular and Hierarchical Structures 47 6.1.1 Automatically Defined Functions 48 6.1.2 Program Architecture and Architecture-Altering 50 6.2 Constraining Structures 51 6.2.1 Enforcing Particular Structures 52 6.2.2 Strongly Typed GP 52 6.2.3 Grammar-based Constraints 53 6.2.4 Constraints and Bias 55 6.3 Developmental Genetic Programming 57 6.4 Strongly Typed Autoconstructive GP with PushGP 59 7 Linear and Graph Genetic Programming 61 7.1 Linear Genetic Programming 61 7.1.1 Motivations 61 7.1.2 Linear GP Representations 62 7.1.3 Linear GP Operators 64 7.2 Graph-Based Genetic Programming 65 7.2.1 Parallel Distributed GP (PDGP) 65 7.2.2 PADO 67 7.2.3 Cartesian GP 67 7.2.4 Evolving Parallel Programs using Indirect Encodings 68 8 Probabilistic Genetic Programming 8.1 Estimation of Distribution Algorithms 69 8.2 Pure EDA GP 71 8.3 Mixing Grammars and Probabilities 74 9 Multi-objective Genetic Programming 75 9.1 Combining Multiple Objectives into a Scalar Fitness Function 75 9.2 Keeping the Objectives Separate 76 9.2.1 Multi-objective Bloat and Complexity Control 77 9.2.2 Other Objectives 78 9.2.3 Non-Pareto Criteria 80 9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80 9.4 Multi-objective Optimisation via Operator Bias 81 10 Fast and Distributed Genetic Programming 83 10.1 Reducing Fitness Evaluations/Increasing their Effectiveness 83 10.2 Reducing Cost of Fitness with Caches 86 10.3 Parallel and Distributed GP are Not Equivalent 88 10.4 Running GP on Parallel Hardware 89 10.4.1 Master–slave GP 89 10.4.2 GP Running on GPUs 90 10.4.3 GP on FPGAs 92 10.4.4 Sub-machine-code GP 93 10.5 Geographically Distributed GP 93 11 GP Theory and its Applications 97 11.1 Mathematical Models 98 11.2 Search Spaces 99 11.3 Bloat 101 11.3.1 Bloat in Theory 101 11.3.2 Bloat Control in Practice 104 III Practical Genetic Programming 12 Applications 12.1 Where GP has Done Well 12.2 Curve Fitting, Data Modelling and Symbolic Regression 12.3 Human Competitive Results – the Humies 12.4 Image and Signal Processing 12.5 Financial Trading, Time Series, and Economic Modelling 12.6 Industrial Process Control 12.7 Medicine, Biology and Bioinformatics 12.8 GP to Create Searchers and Solvers – Hyper-heuristics xiii 12.9 Entertainment and Computer Games 127 12.10The Arts 127 12.11Compression 128 13 Troubleshooting GP 13.1 Is there a Bug in the Code? 13.2 Can you Trust your Results? 13.3 There are No Silver Bullets 13.4 Small Changes can have Big Effects 13.5 Big Changes can have No Effect 13.6 Study your Populations 13.7 Encourage Diversity 13.8 Embrace Approximation 13.9 Control Bloat 13.10 Checkpoint Results 13.11 Report Well 13.12 Convince your Customers 14 Conclusions Tricks of the Trade A Resources A.1 Key Books A.2 Key Journals A.3 Key International Meetings A.4 GP Implementations A.5 On-Line Resources 145 B TinyGP 151 B.1 Overview of TinyGP 151 B.2 Input Data Files for TinyGP 153 B.3 Source Code 154 B.4 Compiling and Running TinyGP 162 Bibliography 167 Inde

Multiobjective differential evolution based on fuzzy performance feedback: Soft constraint handling and its application in antenna designs

The recently emerging Differential Evolution is considered one of the most powerful tools for solving optimization problems. It is a stochastic population-based search approach for optimization over the continuous space. The main advantages of differential evolution are simplicity, robustness and high speed of convergence. Differential evolution is attractive to researchers all over the world as evidenced by recent publications. There are many variants of differential evolution proposed by researchers and differential evolution algorithms are continuously improved in its performance. Performance of differential evolution algorithms depend on the control parameters setting which are problem dependent and time-consuming task. This study proposed a Fuzzy-based Multiobjective Differential Evolution (FMDE) that exploits three performance metrics, specifically hypervolume, spacing, and maximum spread, to measure the state of the evolution process. We apply the fuzzy inference rules to these metrics in order to adaptively adjust the associated control parameters of the chosen mutation strategy used in this algorithm. The proposed FMDE is evaluated on the well known ZDT, DTLZ, and WFG benchmark test suites. The experimental results show that FMDE is competitive with respect to the chosen state-of-the-art multiobjective evolutionary algorithms. The advanced version of FMDE with adaptive crossover rate (AFMDE) is proposed. The proof of concept AFMDE is then applied specifically to the designs of microstrip antenna array. Furthermore, the soft constraint handling technique incorporates with AFMDE is proposed. Soft constraint AFMDE is evaluated on the benchmark constrained problems. AFMDE with soft constraint handling technique is applied to the constrained non-uniform circular antenna array design problem as a case study

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties