Digital image forensics

Digital image forensics is a relatively new research field that aims to expose the origin and composition of, and the history of processing applied to digital images. Hence, the digital image forensics is expected to be of significant importance to our modern society in which the digital media are getting more and more popular. In this thesis, image tampering detection and classification of double JPEG compression are the two major subjects studied. Since any manipulation applied to digital images changes image statistics, identifying statistical artifacts becomes critically important in image forensics. In this thesis, a few typical forensic techniques have been studied. Finally, it is foreseen that the investigations on endless confliction between forensics and anti-forensics are to deepen our understanding on image statistics and advance civilization of our society

A motion-based approach for audio-visual automatic speech recognition

The research work presented in this thesis introduces novel approaches for both visual region of interest extraction and visual feature extraction for use in audio-visual automatic speech recognition. In particular, the speaker‘s movement that occurs during speech is used to isolate the mouth region in video sequences and motionbased features obtained from this region are used to provide new visual features for audio-visual automatic speech recognition. The mouth region extraction approach proposed in this work is shown to give superior performance compared with existing colour-based lip segmentation methods. The new features are obtained from three separate representations of motion in the region of interest, namely the difference in luminance between successive images, block matching based motion vectors and optical flow. The new visual features are found to improve visual-only and audiovisual speech recognition performance when compared with the commonly-used appearance feature-based methods. In addition, a novel approach is proposed for visual feature extraction from either the discrete cosine transform or discrete wavelet transform representations of the mouth region of the speaker. In this work, the image transform is explored from a new viewpoint of data discrimination; in contrast to the more conventional data preservation viewpoint. The main findings of this work are that audio-visual automatic speech recognition systems using the new features extracted from the frequency bands selected according to their discriminatory abilities generally outperform those using features designed for data preservation. To establish the noise robustness of the new features proposed in this work, their performance has been studied in presence of a range of different types of noise and at various signal-to-noise ratios. In these experiments, the audio-visual automatic speech recognition systems based on the new approaches were found to give superior performance both to audio-visual systems using appearance based features and to audio-only speech recognition systems

Word based off-line handwritten Arabic classification and recognition. Design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches.

The design of a machine which reads unconstrained words still remains an unsolved problem. For example, automatic interpretation of handwritten documents by a computer is still under research. Most systems attempt to segment words into letters and read words one character at a time. However, segmenting handwritten words is very difficult. So to avoid this words are treated as a whole. This research investigates a number of features computed from whole words for the recognition of handwritten words in particular. Arabic text classification and recognition is a complicated process compared to Latin and Chinese text recognition systems. This is due to the nature cursiveness of Arabic text. The work presented in this thesis is proposed for word based recognition of handwritten Arabic scripts. This work is divided into three main stages to provide a recognition system. The first stage is the pre-processing, which applies efficient pre-processing methods which are essential for automatic recognition of handwritten documents. In this stage, techniques for detecting baseline and segmenting words in handwritten Arabic text are presented. Then connected components are extracted, and distances between different components are analyzed. The statistical distribution of these distances is then obtained to determine an optimal threshold for word segmentation. The second stage is feature extraction. This stage makes use of the normalized images to extract features that are essential in recognizing the images. Various method of feature extraction are implemented and examined. The third and final stage is the classification. Various classifiers are used for classification such as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition problem studied is the classification of 32492 words using ii the IFN/ENIT database. The results were promising and very encouraging in terms of improved baseline detection and word segmentation for further recognition. Moreover, several feature subsets were examined and a best recognition performance of 81.5% is achieved

Passive Techniques for Detecting and Locating Manipulations in Digital Images

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, leída el 19-11-2020El numero de camaras digitales integradas en dispositivos moviles as como su uso en la vida cotidiana esta en continuo crecimiento. Diariamente gran cantidad de imagenes digitales, generadas o no por este tipo de dispositivos, circulan en Internet o son utilizadas como evidencias o pruebas en procesos judiciales. Como consecuencia, el analisis forense de imagenes digitales cobra importancia en multitud de situaciones de la vida real. El analisis forense de imagenes digitales se divide en dos grandes ramas: autenticidad de imagenes digitales e identificacion de la fuente de adquisicion de una imagen. La primera trata de discernir si una imagen ha sufrido algun procesamiento posterior al de su creacion, es decir, que no haya sido manipulada. La segunda pretende identificar el dispositivo que genero la imagen digital. La verificacion de la autenticidad de imagenes digitales se puedellevar a cabo mediante tecnicas activas y tecnicas pasivas de analisis forense. Las tecnicas activas se fundamentan en que las imagenes digitales cuentan con \marcas" presentes desde su creacion, de forma que cualquier tipo de alteracion que se realice con posterioridad a su generacion, modificara las mismas, y, por tanto, permitiran detectar si ha existido un posible post-proceso o manipulacion...The number of digital cameras integrated into mobile devices as well as their use in everyday life is continuously growing. Every day a large number of digital images, whether generated by this type of device or not, circulate on the Internet or are used as evidence in legal proceedings. Consequently, the forensic analysis of digital images becomes important in many real-life situations. Forensic analysis of digital images is divided into two main branches: authenticity of digital images and identi cation of the source of acquisition of an image. The first attempts to discern whether an image has undergone any processing subsequent to its creation, i.e. that it has not been manipulated. The second aims to identify the device that generated the digital image. Verification of the authenticity of digital images can be carried out using both active and passive forensic analysis techniques. The active techniques are based on the fact that the digital images have "marks"present since their creation so that any type of alteration made after their generation will modify them, and therefore will allow detection if there has been any possible post-processing or manipulation. On the other hand, passive techniques perform the analysis of authenticity by extracting characteristics from the image...Fac. de InformáticaTRUEunpu

Automatic Speech Recognition Using LP-DCTC/DCS Analysis Followed by Morphological Filtering

Front-end feature extraction techniques have long been a critical component in Automatic Speech Recognition (ASR). Nonlinear filtering techniques are becoming increasingly important in this application, and are often better than linear filters at removing noise without distorting speech features. However, design and analysis of nonlinear filters are more difficult than for linear filters. Mathematical morphology, which creates filters based on shape and size characteristics, is a design structure for nonlinear filters. These filters are limited to minimum and maximum operations that introduce a deterministic bias into filtered signals. This work develops filtering structures based on a mathematical morphology that utilizes the bias while emphasizing spectral peaks. The combination of peak emphasis via LP analysis with morphological filtering results in more noise robust speech recognition rates. To help understand the behavior of these pre-processing techniques the deterministic and statistical properties of the morphological filters are compared to the properties of feature extraction techniques that do not employ such algorithms. The robust behavior of these algorithms for automatic speech recognition in the presence of rapidly fluctuating speech signals with additive and convolutional noise is illustrated. Examples of these nonlinear feature extraction techniques are given using the Aurora 2.0 and Aurora 3.0 databases. Features are computed using LP analysis alone to emphasize peaks, morphological filtering alone, or a combination of the two approaches. Although absolute best results are normally obtained using a combination of the two methods, morphological filtering alone is nearly as effective and much more computationally efficient

A motion based approach for audio-visual automatic speech recognition

The research work presented in this thesis introduces novel approaches for both visual region of interest extraction and visual feature extraction for use in audio-visual automatic speech recognition. In particular, the speaker‘s movement that occurs during speech is used to isolate the mouth region in video sequences and motionbased features obtained from this region are used to provide new visual features for audio-visual automatic speech recognition. The mouth region extraction approach proposed in this work is shown to give superior performance compared with existing colour-based lip segmentation methods. The new features are obtained from three separate representations of motion in the region of interest, namely the difference in luminance between successive images, block matching based motion vectors and optical flow. The new visual features are found to improve visual-only and audiovisual speech recognition performance when compared with the commonly-used appearance feature-based methods. In addition, a novel approach is proposed for visual feature extraction from either the discrete cosine transform or discrete wavelet transform representations of the mouth region of the speaker. In this work, the image transform is explored from a new viewpoint of data discrimination; in contrast to the more conventional data preservation viewpoint. The main findings of this work are that audio-visual automatic speech recognition systems using the new features extracted from the frequency bands selected according to their discriminatory abilities generally outperform those using features designed for data preservation. To establish the noise robustness of the new features proposed in this work, their performance has been studied in presence of a range of different types of noise and at various signal-to-noise ratios. In these experiments, the audio-visual automatic speech recognition systems based on the new approaches were found to give superior performance both to audio-visual systems using appearance based features and to audio-only speech recognition systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Digital forensic techniques for the reverse engineering of image acquisition chains

In recent years a number of new methods have been developed to detect image forgery. Most forensic techniques use footprints left on images to predict the history of the images. The images, however, sometimes could have gone through a series of processing and modification through their lifetime. It is therefore difficult to detect image tampering as the footprints could be distorted or removed over a complex chain of operations. In this research we propose digital forensic techniques that allow us to reverse engineer and determine history of images that have gone through chains of image acquisition and reproduction. This thesis presents two different approaches to address the problem. In the first part we propose a novel theoretical framework for the reverse engineering of signal acquisition chains. Based on a simplified chain model, we describe how signals have gone in the chains at different stages using the theory of sampling signals with finite rate of innovation. Under particular conditions, our technique allows to detect whether a given signal has been reacquired through the chain. It also makes possible to predict corresponding important parameters of the chain using acquisition-reconstruction artefacts left on the signal. The second part of the thesis presents our new algorithm for image recapture detection based on edge blurriness. Two overcomplete dictionaries are trained using the K-SVD approach to learn distinctive blurring patterns from sets of single captured and recaptured images. An SVM classifier is then built using dictionary approximation errors and the mean edge spread width from the training images. The algorithm, which requires no user intervention, was tested on a database that included more than 2500 high quality recaptured images. Our results show that our method achieves a performance rate that exceeds 99% for recaptured images and 94% for single captured images.Open Acces

Self Designing Pattern Recognition System Employing Multistage Classification

Recently, pattern recognition/classification has received a considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, etc. In most of these applications, it is desirable to maintain the classification accuracy in the presence of corrupted and/or incomplete data. The quality of a given classification technique is measured by the computational complexity, execution time of algorithms, and the number of patterns that can be classified correctly despite any distortion. Some classification techniques that are introduced in the literature are described in Chapter one. In this dissertation, a pattern recognition approach that can be designed to have evolutionary learning by developing the features and selecting the criteria that are best suited for the recognition problem under consideration is proposed. Chapter two presents some of the features used in developing the set of criteria employed by the system to recognize different types of signals. It also presents some of the preprocessing techniques used by the system. The system operates in two modes, namely, the learning (training) mode, and the running mode. In the learning mode, the original and preprocessed signals are projected into different transform domains. The technique automatically tests many criteria over the range of parameters for each criterion. A large number of criteria are developed from the features extracted from these domains. The optimum set of criteria, satisfying specific conditions, is selected. This set of criteria is employed by the system to recognize the original or noisy signals in the running mode. The modes of operation and the classification structures employed by the system are described in details in Chapter three. The proposed pattern recognition system is capable of recognizing an enormously large number of patterns by virtue of the fact that it analyzes the signal in different domains and explores the distinguishing characteristics in each of these domains. In other words, this approach uses available information and extracts more characteristics from the signals, for classification purposes, by projecting the signal in different domains. Some experimental results are given in Chapter four showing the effect of using mathematical transforms in conjunction with preprocessing techniques on the classification accuracy. A comparison between some of the classification approaches, in terms of classification rate in case of distortion, is also given. A sample of experimental implementations is presented in chapter 5 and chapter 6 to illustrate the performance of the proposed pattern recognition system. Preliminary results given confirm the superior performance of the proposed technique relative to the single transform neural network and multi-input neural network approaches for image classification in the presence of additive noise