Classifiers and machine learning techniques for image processing and computer vision

Orientador: Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto da ComputaçãoResumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação.Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques.DoutoradoEngenharia de ComputaçãoDoutor em Ciência da Computaçã

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process

A Proposal for a European Cybersecurity Taxonomy

The Commission made a commitment in the Communication adopted in September 2018 (COM(2018) 630 final) to launch a pilot phase under Horizon 2020 to help bring national cybersecurity centres together into a network. In this context, the goal of this document is that of aligning the cybersecurity terminologies, definitions and domains into a coherent and comprehensive taxonomy to facilitate the categorisation of EU cybersecurity competencies.JRC.E.3-Cyber and Digital Citizens' Securit

Exploring Biomedical Video Source Identification: Transitioning from Fuzzy-Based Systems to Machine Learning Models

In recent years, the field of biomedical video source identification has witnessed a significant evolution driven by advances in both fuzzy-based systems and machine learning models. This paper presents a comprehensive survey of the current state of the art in this domain, highlighting the transition from traditional fuzzy-based approaches to the emerging dominance of machine learning techniques. Biomedical videos have become integral in various aspects of healthcare, from medical imaging and diagnostics to surgical procedures and patient monitoring. The accurate identification of the sources of these videos is of paramount importance for quality control, accountability, and ensuring the integrity of medical data. In this context, source identification plays a critical role in establishing the authenticity and origin of biomedical videos. This survey delves into the evolution of source identification methods, covering the foundational principles of fuzzy-based systems and their applications in the biomedical context. It explores how linguistic variables and expert knowledge were employed to model video sources, and discusses the strengths and limitations of these early approaches. By surveying existing methodologies and databases, this paper contributes to a broader understanding of the field’s progress and challenges

Employing optical flow on convolutional recurrent structures for deepfake detection

Deepfakes, or artificially generated audiovisual renderings, can be used to defame a public figure or influence public opinion. With the recent discovery of generative adversarial networks, an attacker using a normal desktop computer fitted with an off-the-shelf graphics processing unit can make renditions realistic enough to easily fool a human observer. Detecting deepfakes is thus becoming vital for reporters, social networks, and the general public. Preliminary research introduced simple, yet surprisingly efficient digital forensic methods for visual deepfake detection. These methods combined convolutional latent representations with bidirectional recurrent structures and entropy-based cost functions. The latent representations for the video are carefully chosen to extract semantically rich information from the recordings. By feeding these into a recurrent framework, we were able to sequentially detect both spatial and temporal signatures of deepfake renditions. The entropy-based cost functions work well in isolation as well as in context with traditional cost functions. However, re-enactment based forgery is getting harder to detect with newer generation techniques ameliorating on temporal ironing and background stability. As these generative models involve the use of a learnable flow mapping network from the driving video to the target face, we hypothesized that the inclusion of edge maps in addition to dense flow maps near the facial region provides the model with finer details to make an informed classification. Methods were demonstrated on the FaceForensics++, Celeb-DF, and DFDC-mini (custom-made) video datasets, achieving new benchmarks in all categories. We also perform extensive studies to evaluate on adversaries and demonstrate generalization to new domains, consequently gaining further insight into the effectiveness of the new architectures

DEEP LEARNING FOR FORENSICS

The advent of media sharing platforms and the easy availability of advanced photo or video editing software have resulted in a large quantity of manipulated images and videos being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of fake news and misinformation is growing. Therefore, detecting manipulation has become an emerging necessity. Different from traditional classification, semantic object detection or segmentation, manipulation detection/classification pays more attention to low-level tampering artifacts than to semantic content. The main challenges in this problem include (a) investigating features to reveal tampering artifacts, (b) developing generic models which are robust to a large scale of post-processing methods, (c) applying algorithms to higher resolution in real scenarios and (d) handling the new emerging manipulation techniques. In this dissertation, we propose approaches to tackling these challenges. Manipulation detection utilizes both low-level tamper artifacts and semantic contents, suggesting that richer features needed to be harnessed to reveal more evidence. To learn rich features, we propose a two-stream Faster R-CNN network and train it end-to-end to detect the tampered regions given a manipulated image. Experiments on four standard image manipulation datasets demonstrate that our two-stream framework outperforms each individual stream, and also achieves state-of-the-art performance compared to alternative methods with robustness to resizing and compression. Additionally, to extend manipulation detection from image to video, we introduce VIDNet, Video Inpainting Detection Network, which contains an encoder-decoder architecture with a quad-directional local attention module. To reveal artifacts encoded in compression, VIDNet additionally takes in Error Level Analysis (ELA) frames to augment RGB frames, producing multimodal features at different levels with an encoder. Besides, to improve the generalization of manipulation detection model, we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Extensive experimental results validate our proposal. Furthermore, to apply deep learning models to high resolution scenarios efficiently, we treat the problem as a mask refinement given a coarse low resolution prediction. We propose to convert the regions of interest into strip images and compute a boundary prediction in the strip domain. Extensive experiments on both the public and a newly created high resolution dataset strongly validate our approach. Finally, to handle new emerging manipulation techniques while preserving performance on learned manipulation, we investigate incremental learning. We propose a multi-model and multi-level knowledge distillation strategy to preserve performance on old categories while training on new categories. Experiments on standard incremental learning benchmarks show that our method improves the overall performance over standard distillation techniques

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area