An Analysis on Adversarial Machine Learning: Methods and Applications

Deep learning has witnessed astonishing advancement in the last decade and revolutionized many fields ranging from computer vision to natural language processing. A prominent field of research that enabled such achievements is adversarial learning, investigating the behavior and functionality of a learning model in presence of an adversary. Adversarial learning consists of two major trends. The first trend analyzes the susceptibility of machine learning models to manipulation in the decision-making process and aims to improve the robustness to such manipulations. The second trend exploits adversarial games between components of the model to enhance the learning process. This dissertation aims to provide an analysis on these two sides of adversarial learning and harness their potential for improving the robustness and generalization of deep models. In the first part of the dissertation, we study the adversarial susceptibility of deep learning models. We provide an empirical analysis on the extent of vulnerability by proposing two adversarial attacks that explore the geometric and frequency-domain characteristics of inputs to manipulate deep decisions. Afterward, we formalize the susceptibility of deep networks using the first-order approximation of the predictions and extend the theory to the ensemble classification scheme. Inspired by theoretical findings, we formalize a reliable and practical defense against adversarial examples to robustify ensembles. We extend this part by investigating the shortcomings of \gls{at} and highlight that the popular momentum stochastic gradient descent, developed essentially for natural training, is not proper for optimization in adversarial training since it is not designed to be robust against the chaotic behavior of gradients in this setup. Motivated by these observations, we develop an optimization method that is more suitable for adversarial training. In the second part of the dissertation, we harness adversarial learning to enhance the generalization and performance of deep networks in discriminative and generative tasks. We develop several models for biometric identification including fingerprint distortion rectification and latent fingerprint reconstruction. In particular, we develop a ridge reconstruction model based on generative adversarial networks that estimates the missing ridge information in latent fingerprints. We introduce a novel modification that enables the generator network to preserve the ID information during the reconstruction process. To address the scarcity of data, {\it e.g.}, in latent fingerprint analysis, we develop a supervised augmentation technique that combines input examples based on their salient regions. Our findings advocate that adversarial learning improves the performance and reliability of deep networks in a wide range of applications

Napodobení a výroba vzhledu pomocí diferencovatelných materiálových modelů

Výpočetní deriváty kódu - s kódem - jsou jedním z klíčových aktivátorů revoluce strojového učení. V počítačové grafice umožňuje automatická diferenciace řešit problémy s inverzním renderingem, kde se z jednoho nebo několika vstupních snímků získávají parametry jako je odrazovost objektu, poloha nebo koeficienty rozptylu a absorpce ob- jemu. V této práci zvažujeme problémy s přizpůsobením vzhledu a s výrobou, které lze uvést jako příklady problémů s inverzním renderingem. Zatímco optimalizace založená na gradientu, kterou umožňují diferencovatelné programy, má potenciál přinést velmi dobré výsledky, vyžaduje správné využití. Diferenciovatelný rendering není řešením problémů typu brokovnice. Diskutujeme jak teoretické koncepty, tak praktickou implementaci dife- rencovatelných renderingových algoritmů a ukazujeme, jak se spojují s různými problémy s přizpůsobením vzhledu. 1Computing derivatives of code - with code - is one of the key enablers of the machine learning revolution. In computer graphics, automatic differentiation allows to solve in- verse rendering problems. There, parameters such as an objects reflectance, position, or the scattering- and absorption coefficients of a volume, are recovered from one or several input images. In this work, we consider appearance matching and fabrication problems, that can be cast as instances of inverse rendering problems. While gradient-based opti- mization that is enabled by differentiable programs has the potential to yield very good results, it requires proper handling - differentiable rendering is not a shotgun-type prob- lem solver. We discuss both theoretical concepts and the practical implementation of differentiable rendering algorithms, and show how they connect to different appearance matching problems. 1Katedra softwaru a výuky informatikyDepartment of Software and Computer Science EducationMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Marketised forensic DNA-profiling in England & Wales

Previously held under moratorium from 20 March 2020 until 20 March 2022Forensic science provision in the United Kingdom has undergone significant, though uneven, development during the past decade. In England and Wales, forensic expertise is now delivered by way of a commercial market, whilst similar provision in Scotland, and Northern Ireland, remains within the public sector. As a result of marketisation, police forces (and other forensic ‘customers’) have become increasingly concerned with measuring economic value, whilst forensic science providers have been required to maintain an efficient, high-quality service that conforms to the overarching regulations. Early studies suggest that these structural, and regulatory, developments have had a marked impact upon the field of forensic DNA analysis, and may affect the way in which expert DNA evidence is constructed. This empirical research project seeks to assess the impact that these public policy, and organizational, developments, have had on the perspectives of forensic DNA-profiling experts. The project focuses on the perceived links between governance structures and the performance of forensic expertise, through the construction of analytical, and evaluative, reports. The study also considers the reported impacts of overarching regulatory incursions. The purpose of this unique study is to gain a clearer understanding of the ways in which forensic DNA profilers have responded to policy-driven structural changes, and to assess the perceived effects of resulting adaptations. The project has uncovered valuable data, demonstrating that respondents regard DNA reporting and evaluation in relation to serious crime as conforming to the highest scientific standards. However, the ways in which ‘volume’ crime cases are perceived to have been dealt with may raise more pressing questions. Indeed, certain trends are identified within the respondent’s testimony, based upon their experiences of the forensic market, which may raise concerns. Particular developments (such as the perception of case fragmentation and de-skilling, and concerns relating to the production of streamlined reports) could - if accurate - impact on the quality of expert opinion, and may potentially subvert the courts’ ability to arrive at sound determinations on questions of fact.Forensic science provision in the United Kingdom has undergone significant, though uneven, development during the past decade. In England and Wales, forensic expertise is now delivered by way of a commercial market, whilst similar provision in Scotland, and Northern Ireland, remains within the public sector. As a result of marketisation, police forces (and other forensic ‘customers’) have become increasingly concerned with measuring economic value, whilst forensic science providers have been required to maintain an efficient, high-quality service that conforms to the overarching regulations. Early studies suggest that these structural, and regulatory, developments have had a marked impact upon the field of forensic DNA analysis, and may affect the way in which expert DNA evidence is constructed. This empirical research project seeks to assess the impact that these public policy, and organizational, developments, have had on the perspectives of forensic DNA-profiling experts. The project focuses on the perceived links between governance structures and the performance of forensic expertise, through the construction of analytical, and evaluative, reports. The study also considers the reported impacts of overarching regulatory incursions. The purpose of this unique study is to gain a clearer understanding of the ways in which forensic DNA profilers have responded to policy-driven structural changes, and to assess the perceived effects of resulting adaptations. The project has uncovered valuable data, demonstrating that respondents regard DNA reporting and evaluation in relation to serious crime as conforming to the highest scientific standards. However, the ways in which ‘volume’ crime cases are perceived to have been dealt with may raise more pressing questions. Indeed, certain trends are identified within the respondent’s testimony, based upon their experiences of the forensic market, which may raise concerns. Particular developments (such as the perception of case fragmentation and de-skilling, and concerns relating to the production of streamlined reports) could - if accurate - impact on the quality of expert opinion, and may potentially subvert the courts’ ability to arrive at sound determinations on questions of fact

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

Development of neutron resonance densitometry at the GELINA TOF facility

Neutrons can be used as a tool to study properties of materials and objects. An evolving activity in this field concerns the existence of resonances in neutron induced reaction cross sections. These resonance structures are the basis of two analytical methods which have been developed at the EC-JRC-IRMM: Neutron Resonance Capture Analysis (NRCA) and Neutron Resonance Transmission Analysis (NRTA). They have been applied to determine the elemental composition of archaeological objects and to characterize nuclear reference materials. A combination of NRTA and NRCA together with Prompt Gamma Neutron Analysis, referred to as Neutron Resonance Densitometry (NRD), is being studied as a non-destructive method to characterize particle-like debris of melted fuel that is formed in severe nuclear accidents such as the one which occurred at the Fukushima Daiichi nuclear power plants. This study is part of a collaboration between JAEA and EC-JRC-IRMM. In this contribution the basic principles of NRTA and NRCA are explained based on the experience in the use of these methods at the time-of-flight facility GELINA of the EC-JRC-IRMM. Specific problems related to the analysis of samples resulting from melted fuel are discussed. The programme to study and solve these problems is described and results of a first measurement campaign at GELINA are given.JRC.D.4-Standards for Nuclear Safety, Security and Safeguard