Aplicación de PCA y técnicas bayesianas a la clasificación de píxeles basada en color

En este trabajo se propone un método para la clasificación de píxeles en base a su color. A partir de un conjunto de variables que caracterizan un píxel según su color se determinará cuáles de éstas son las más representativas y se realizará la clasificación propiamente dicha. Para ello nuestro método consta de dos fases: en la primera se aplica PCA para obtener el conjunto de variables características más informativas; en la segunda, dichas variables se utilizan como patrones de las clases de un clasificador bayesiano. El método se ilustra a través de varios experimentos

Aplicación de PCA y técnicas bayesianas a la clasificación de píxeles basada en color

En este trabajo se propone un método para la clasificación de píxeles en base a su color. A partir de un conjunto de variables que caracterizan un píxel según su color se determinará cuáles de éstas son las más representativas y se realizará la clasificación propiamente dicha. Para ello nuestro método consta de dos fases: en la primera se aplica PCA para obtener el conjunto de variables características más informativas; en la segunda, dichas variables se utilizan como patrones de las clases de un clasificador bayesiano. El método se ilustra a través de varios experimentos

Image statistical frameworks for digital image forensics

The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet personal computers as well as high-speed networks have made a digital image a conventional medium for visual information. Creation, duplication, distribution, or tampering of such a medium can be easily done, which calls for the necessity to be able to trace back the authenticity or history of the medium. Digital image forensics is an emerging research area that aims to resolve the imposed problem and has grown in popularity over the past decade. On the other hand, anti-forensics has emerged over the past few years as a relatively new branch of research, aiming at revealing the weakness of the forensic technology. These two sides of research move digital image forensic technologies to the next higher level. Three major contributions are presented in this dissertation as follows. First, an effective multi-resolution image statistical framework for digital image forensics of passive-blind nature is presented in the frequency domain. The image statistical framework is generated by applying Markovian rake transform to image luminance component. Markovian rake transform is the applications of Markov process to difference arrays which are derived from the quantized block discrete cosine transform 2-D arrays with multiple block sizes. The efficacy and universality of the framework is then evaluated in two major applications of digital image forensics: 1) digital image tampering detection; 2) classification of computer graphics and photographic images. Second, a simple yet effective anti-forensic scheme is proposed, capable of obfuscating double JPEG compression artifacts, which may vital information for image forensics, for instance, digital image tampering detection. Shrink-and-zoom (SAZ) attack, the proposed scheme, is simply based on image resizing and bilinear interpolation. The effectiveness of SAZ has been evaluated over two promising double JPEG compression schemes and the outcome reveals that the proposed scheme is effective, especially in the cases that the first quality factor is lower than the second quality factor. Third, an advanced textural image statistical framework in the spatial domain is proposed, utilizing local binary pattern (LBP) schemes to model local image statistics on various kinds of residual images including higher-order ones. The proposed framework can be implemented either in single- or multi-resolution setting depending on the nature of application of interest. The efficacy of the proposed framework is evaluated on two forensic applications: 1) steganalysis with emphasis on HUGO (Highly Undetectable Steganography), an advanced steganographic scheme embedding hidden data in a content-adaptive manner locally into some image regions which are difficult for modeling image statics; 2) image recapture detection (IRD). The outcomes of the evaluations suggest that the proposed framework is effective, not only for detecting local changes which is in line with the nature of HUGO, but also for detecting global difference (the nature of IRD)

Face Spoof Detection Using Convolutional Neural Networks

Department: Engineering and applied sciences Major: 060509 – Computer Science Major: Software of Computer Systems and Networks Supervisor: PhD, Associate Professor Leyla MuradkhanliIn recent years, the use of facial recognition technology has become increasingly prevalent, finding application in various areas, including security, authentication, and access management. With the extensive employment of face recognition technology has come an increase in the prevalence of face spoofing cases, wherein offenders manipulate the system with unauthentic facial information. The emergence of this issue poses a major risk to the dependability and protection of facial recognition technology. This calls for the development of advanced and robust techniques to detect face spoofing effectively. This thesis suggests a technique that employs convolutional neural networks (CNN) to identify fraudulent facial manipulation. The proposed method comprises teaching an intricate neural network using a comprehensive compilation of genuine and fabricated facial images. Two streams are employed in this process. RGB images are transformed to grayscale images in the first stream, and then facial reflection features are extracted. Face color features from RGB images are extracted in the second stream. These two characteristics are then combined and utilized to identify face spoofing. The structure of CNN includes several layers of convolution and pooling, which enable it to identify distinguishing features in the input images. Following its training, the model is employed to differentiate a presented facial image into either authentic or fraudulent. To determine the efficacy of the proposed technique, I employ a standardized data set for identifying counterfeit or altered facial attributes. The proposed approach has the capability to achieve an average precision rate of 89% while being applied to the provided data set. The suggested method presents various benefits compared to current techniques for detecting face spoofing. To start with, utilizing a deep CNN empowers the model to acquire intricate and discerning characteristics from the input images, thus augmenting the precision of the categorization mission. Additionally, the suggested method is effective in terms of computational requirements, enabling its utilization in real-time scenarios. The proposed methodology is able to withstand a range of fraudulent tactics used on facial recognition systems, such as print and replay attacks. The findings from this study aid in the progression of face recognition technology by enhancing the accuracy and dependability of fraud detection systems. These improved systems have practical applications in security measures, biometric identification, and digital criminal investigations. The suggested method could substantially enhance the dependability and safety of facial recognition systems, consequently boosting their functional value and credibility

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Computer analysis for registration and change detection of retinal images

The current system of retinal screening is manual; It requires repetitive examination of a large number of retinal images by professional optometrists who try to identify the presence of abnormalities. As a result of the manual and repetitive nature of such examination, there is a possibility for error in diagnosis, in particular in the case when the progression of disease is slight. As the sight is an extremely important sense, any tools which can improve the probability of detecting disease could be considered beneficial. Moreover, the early detection of ophthalmic anomalies can prevent the impairment or loss of vision. The study reported in this Thesis investigates computer vision and image processing techniques to analyse retinal images automatically, in particular for diabetic retinopathy disease which causes blindness. This analysis aims to automate registration to detect differences between a pair of images taken at different times. These differences could be the result of disease progression or, occasionally, simply the presence of artefacts. The resulting methods from this study, will be therefore used to build a software tool to aid the diagnosis process undertaken by ophthalmologists. The research also presents a number of algorithms for the enhancement and visualisation of information present within the retinal images, which under normal situations would be invisible to the viewer; For instance, in the case of slight disease progression or in the case of similar levels of contrast between images, making it difficult for the human eye to see or to distinguish any variations. This study also presents a number of developed methods for computer analysis of retinal images. These methods include a colour distance measurement algorithm, detection of bifurcations and their cross points in retina, image registration, and change detection. The overall analysis in this study can be classified to four stages: image enhancement, landmarks detection, registration, and change detection. The study has showed that the methods developed can achieve automatic, efficient, accurate, and robust implementation