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

Face analysis and deepfake detection

This thesis concerns deep-learning-based face-related research topics. We explore how to improve the performance of several face systems when confronting challenging variations. In Chapter 1, we provide an introduction and background information on the theme, and we list the main research questions of this dissertation. In Chapter 2, we provide a synthetic face data generator with fully controlled variations and proposed a detailed experimental comparison of main characteristics that influence face detection performance. The result shows that our synthetic dataset could complement face detectors to become more robust against specific features in the real world. Our analysis also reveals that a variety of data augmentation is necessary to address differences in performance. In Chapter 3, we propose an age estimation method for handling large pose variations for unconstrained face images. A Wasserstein-based GAN model is used to complete the full uv texture presentation. The proposed AgeGAN method simultaneously learns to capture the facial uv texture map and age characteristics.In Chapter 4, we propose a maximum mean discrepancy (MMD) based cross-domain face forgery detection. The center and triplet losses are also incorporated to ensure that the learned features are shared by multiple domains and provide better generalization abilities to unseen deep fake samples. In Chapter 5, we introduce an end-to-end framework to predict ages from face videos. Clustering based transfer learning is used to provide proper prediction for imbalanced datasets

Percepção do ambiente urbano e navegação usando visão robótica : concepção e implementação aplicado à veículo autônomo

Orientadores: Janito Vaqueiro Ferreira, Alessandro Corrêa VictorinoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: O desenvolvimento de veículos autônomos capazes de se locomover em ruas urbanas pode proporcionar importantes benefícios na redução de acidentes, no aumentando da qualidade de vida e também na redução de custos. Veículos inteligentes, por exemplo, frequentemente baseiam suas decisões em observações obtidas a partir de vários sensores tais como LIDAR, GPS e câmeras. Atualmente, sensores de câmera têm recebido grande atenção pelo motivo de que eles são de baixo custo, fáceis de utilizar e fornecem dados com rica informação. Ambientes urbanos representam um interessante mas também desafiador cenário neste contexto, onde o traçado das ruas podem ser muito complexos, a presença de objetos tais como árvores, bicicletas, veículos podem gerar observações parciais e também estas observações são muitas vezes ruidosas ou ainda perdidas devido a completas oclusões. Portanto, o processo de percepção por natureza precisa ser capaz de lidar com a incerteza no conhecimento do mundo em torno do veículo. Nesta tese, este problema de percepção é analisado para a condução nos ambientes urbanos associado com a capacidade de realizar um deslocamento seguro baseado no processo de tomada de decisão em navegação autônoma. Projeta-se um sistema de percepção que permita veículos robóticos a trafegar autonomamente nas ruas, sem a necessidade de adaptar a infraestrutura, sem o conhecimento prévio do ambiente e considerando a presença de objetos dinâmicos tais como veículos. Propõe-se um novo método baseado em aprendizado de máquina para extrair o contexto semântico usando um par de imagens estéreo, a qual é vinculada a uma grade de ocupação evidencial que modela as incertezas de um ambiente urbano desconhecido, aplicando a teoria de Dempster-Shafer. Para a tomada de decisão no planejamento do caminho, aplica-se a abordagem dos tentáculos virtuais para gerar possíveis caminhos a partir do centro de referencia do veículo e com base nisto, duas novas estratégias são propostas. Em primeiro, uma nova estratégia para escolher o caminho correto para melhor evitar obstáculos e seguir a tarefa local no contexto da navegação hibrida e, em segundo, um novo controle de malha fechada baseado na odometria visual e o tentáculo virtual é modelado para execução do seguimento de caminho. Finalmente, um completo sistema automotivo integrando os modelos de percepção, planejamento e controle são implementados e validados experimentalmente em condições reais usando um veículo autônomo experimental, onde os resultados mostram que a abordagem desenvolvida realiza com sucesso uma segura navegação local com base em sensores de câmeraAbstract: The development of autonomous vehicles capable of getting around on urban roads can provide important benefits in reducing accidents, in increasing life comfort and also in providing cost savings. Intelligent vehicles for example often base their decisions on observations obtained from various sensors such as LIDAR, GPS and Cameras. Actually, camera sensors have been receiving large attention due to they are cheap, easy to employ and provide rich data information. Inner-city environments represent an interesting but also very challenging scenario in this context, where the road layout may be very complex, the presence of objects such as trees, bicycles, cars might generate partial observations and also these observations are often noisy or even missing due to heavy occlusions. Thus, perception process by nature needs to be able to deal with uncertainties in the knowledge of the world around the car. While highway navigation and autonomous driving using a prior knowledge of the environment have been demonstrating successfully, understanding and navigating general inner-city scenarios with little prior knowledge remains an unsolved problem. In this thesis, this perception problem is analyzed for driving in the inner-city environments associated with the capacity to perform a safe displacement based on decision-making process in autonomous navigation. It is designed a perception system that allows robotic-cars to drive autonomously on roads, without the need to adapt the infrastructure, without requiring previous knowledge of the environment and considering the presence of dynamic objects such as cars. It is proposed a novel method based on machine learning to extract the semantic context using a pair of stereo images, which is merged in an evidential grid to model the uncertainties of an unknown urban environment, applying the Dempster-Shafer theory. To make decisions in path-planning, it is applied the virtual tentacle approach to generate possible paths starting from ego-referenced car and based on it, two news strategies are proposed. First one, a new strategy to select the correct path to better avoid obstacles and to follow the local task in the context of hybrid navigation, and second, a new closed loop control based on visual odometry and virtual tentacle is modeled to path-following execution. Finally, a complete automotive system integrating the perception, path-planning and control modules are implemented and experimentally validated in real situations using an experimental autonomous car, where the results show that the developed approach successfully performs a safe local navigation based on camera sensorsDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânic

Cartographie dense basée sur une représentation compacte RGB-D dédiée à la navigation autonome

Our aim is concentrated around building ego-centric topometric maps represented as a graph of keyframe nodes which can be efficiently used by autonomous agents. The keyframe nodes which combines a spherical image and a depth map (augmented visual sphere) synthesises information collected in a local area of space by an embedded acquisition system. The representation of the global environment consists of a collection of augmented visual spheres that provide the necessary coverage of an operational area. A "pose" graph that links these spheres together in six degrees of freedom, also defines the domain potentially exploitable for navigation tasks in real time. As part of this research, an approach to map-based representation has been proposed by considering the following issues : how to robustly apply visual odometry by making the most of both photometric and ; geometric information available from our augmented spherical database ; how to determine the quantity and optimal placement of these augmented spheres to cover an environment completely ; how tomodel sensor uncertainties and update the dense infomation of the augmented spheres ; how to compactly represent the information contained in the augmented sphere to ensure robustness, accuracy and stability along an explored trajectory by making use of saliency maps.Dans ce travail, nous proposons une représentation efficace de l’environnement adaptée à la problématique de la navigation autonome. Cette représentation topométrique est constituée d’un graphe de sphères de vision augmentées d’informations de profondeur. Localement la sphère de vision augmentée constitue une représentation égocentrée complète de l’environnement proche. Le graphe de sphères permet de couvrir un environnement de grande taille et d’en assurer la représentation. Les "poses" à 6 degrés de liberté calculées entre sphères sont facilement exploitables par des tâches de navigation en temps réel. Dans cette thèse, les problématiques suivantes ont été considérées : Comment intégrer des informations géométriques et photométriques dans une approche d’odométrie visuelle robuste ; comment déterminer le nombre et le placement des sphères augmentées pour représenter un environnement de façon complète ; comment modéliser les incertitudes pour fusionner les observations dans le but d’augmenter la précision de la représentation ; comment utiliser des cartes de saillances pour augmenter la précision et la stabilité du processus d’odométrie visuelle

Machine cosmology: investigating the dark sector through novel inference methods

Cosmology during the last few decades has experienced an influx of new theory and observations, pushed forward by ever-increasing capabilities of current and upcoming large-scale surveys, computational and methodological capabilities, and new theoretical work being fueled by these latter factors. Observational measurements often carry uncertainties from noise or random processes, with inference methods being concerned with inverse probability as the quest to explore underlying distributions of data. Over the same time frame, Bayesian statistics has thus quickly found itself in a central role in cosmological analysis, as the field is rife with inverse problems such as hypothesis testing, model selection, and parameter estimation. More recently, inference models from the field of machine learning have also experienced a surge in applications to cosmology. We delve into the utility of such inference methods for challenges in cosmology in different degrees of granularity and focusing on the dark sector of our Universe, traveling from the largest scale to more local problems in the process. Starting in the area of cosmological parameter estimation, we develop a novel parallel-iterative parameter estimation method rooted in Bayesian nonparametrics and recent developments in variational inference from the field of machine learning in Chapter 2. In doing so, we propose, implement, and test a new approach to fast high-dimensional parameter estimation in an embarrassingly parallel manner. For this work, we make use of large-scale supercomputing facilities to speed up the functional extraction of cosmological parameter posteriors based on data from the Dark Energy Survey. Next, we concentrate on the dark energy equation of state in Chapter 3, stress-testing its imprint on type Ia supernovae measurements through an introduced random curve generator for smooth function perturbation. We then investigate the robustness of standard model analyses based on such data with regard to deviations from a cosmological constant in the form of a redshift-dependent equation of state. With regard to large-scale structure, we show the advantages of density ridges as curvilinear principal curves from Dark Energy Survey weak lensing data for cosmic trough identification in Chapter 4. Denoising large-scale structure in this way allows for the more fine-grained identification of structural components in the cosmic web. We also compare the results of our extended version of the subspace-constrained mean shift algorithm to curvelet denoising as an alternative method, as well as trough structure from measurements of the foreground matter density field. Lastly, in the area of galaxy formation and evolution, we combine analytic formalisms and machine learning methods in a hybrid prediction framework in Chapter 5. We use a two-step process to populate dark matter haloes taken from the SIMBA cosmological simulation with baryonic galaxy properties of interest. For this purpose, we use the equilibrium model of galaxy evolution as a precursory module to enable an improved prediction of remaining baryonic properties as a way to quickly complete cosmological simulations

Learning visual representations with neural networks for video captioning and image generation

La recherche sur les réseaux de neurones a permis de réaliser de larges progrès durant la dernière décennie. Non seulement les réseaux de neurones ont été appliqués avec succès pour résoudre des problèmes de plus en plus complexes; mais ils sont aussi devenus l’approche dominante dans les domaines où ils ont été testés tels que la compréhension du langage, les agents jouant à des jeux de manière automatique ou encore la vision par ordinateur, grâce à leurs capacités calculatoires et leurs efficacités statistiques. La présente thèse étudie les réseaux de neurones appliqués à des problèmes en vision par ordinateur, où les représentations sémantiques abstraites jouent un rôle fondamental. Nous démontrerons, à la fois par la théorie et par l’expérimentation, la capacité des réseaux de neurones à apprendre de telles représentations à partir de données, avec ou sans supervision. Le contenu de la thèse est divisé en deux parties. La première partie étudie les réseaux de neurones appliqués à la description de vidéo en langage naturel, nécessitant l’apprentissage de représentation visuelle. Le premier modèle proposé permet d’avoir une attention dynamique sur les différentes trames de la vidéo lors de la génération de la description textuelle pour de courtes vidéos. Ce modèle est ensuite amélioré par l’introduction d’une opération de convolution récurrente. Par la suite, la dernière section de cette partie identifie un problème fondamental dans la description de vidéo en langage naturel et propose un nouveau type de métrique d’évaluation qui peut être utilisé empiriquement comme un oracle afin d’analyser les performances de modèles concernant cette tâche. La deuxième partie se concentre sur l’apprentissage non-supervisé et étudie une famille de modèles capables de générer des images. En particulier, l’accent est mis sur les “Neural Autoregressive Density Estimators (NADEs), une famille de modèles probabilistes pour les images naturelles. Ce travail met tout d’abord en évidence une connection entre les modèles NADEs et les réseaux stochastiques génératifs (GSN). De plus, une amélioration des modèles NADEs standards est proposée. Dénommés NADEs itératifs, cette amélioration introduit plusieurs itérations lors de l’inférence du modèle NADEs tout en préservant son nombre de paramètres. Débutant par une revue chronologique, ce travail se termine par un résumé des récents développements en lien avec les contributions présentées dans les deux parties principales, concernant les problèmes d’apprentissage de représentation sémantiques pour les images et les vidéos. De prometteuses directions de recherche sont envisagées.The past decade has been marked as a golden era of neural network research. Not only have neural networks been successfully applied to solve more and more challenging real- world problems, but also they have become the dominant approach in many of the places where they have been tested. These places include, for instance, language understanding, game playing, and computer vision, thanks to neural networks’ superiority in computational efficiency and statistical capacity. This thesis applies neural networks to problems in computer vision where high-level and semantically meaningful representations play a fundamental role. It demonstrates both in theory and in experiment the ability to learn such representations from data with and without supervision. The main content of the thesis is divided into two parts. The first part studies neural networks in the context of learning visual representations for the task of video captioning. Models are developed to dynamically focus on different frames while generating a natural language description of a short video. Such a model is further improved by recurrent convolutional operations. The end of this part identifies fundamental challenges in video captioning and proposes a new type of evaluation metric that may be used experimentally as an oracle to benchmark performance. The second part studies the family of models that generate images. While the first part is supervised, this part is unsupervised. The focus of it is the popular family of Neural Autoregressive Density Estimators (NADEs), a tractable probabilistic model for natural images. This work first makes a connection between NADEs and Generative Stochastic Networks (GSNs). The standard NADE is improved by introducing multiple iterations in its inference without increasing the number of parameters, which is dubbed iterative NADE. With a historical view at the beginning, this work ends with a summary of recent development for work discussed in the first two parts around the central topic of learning visual representations for images and videos. A bright future is envisioned at the end

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

New Directions for Contact Integrators

Contact integrators are a family of geometric numerical schemes which guarantee the conservation of the contact structure. In this work we review the construction of both the variational and Hamiltonian versions of these methods. We illustrate some of the advantages of geometric integration in the dissipative setting by focusing on models inspired by recent studies in celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282

Classification with class-independent quality information for biometric verification

Biometric identity verification systems frequently face the challenges of non-controlled conditions of data acquisition. Under such conditions biometric signals may suffer from quality degradation due to extraneous, identity-independent factors. It has been demonstrated in numerous reports that a degradation of biometric signal quality is a frequent cause of significant deterioration of classification performance, also in multiple-classifier, multimodal systems, which systematically outperform their single-classifier counterparts. Seeking to improve the robustness of classifiers to degraded data quality, researchers started to introduce measures of signal quality into the classification process. In the existing approaches, the role of class-independent quality information is governed by intuitive rather than mathematical notions, resulting in a clearly drawn distinction between the single-, multiple-classifier and multimodal approaches. The application of quality measures in a multiple-classifier system has received far more attention, with a dominant intuitive notion that a classifier that has data of higher quality at its disposal ought to be more credible than a classifier that operates on noisy signals. In the case of single-classifier systems a quality-based selection of models, classifiers or thresholds has been proposed. In both cases, quality measures have the function of meta-information which supervises but not intervenes with the actual classifier or classifiers employed to assign class labels to modality-specific and class-selective features. In this thesis we argue that in fact the very same mechanism governs the use of quality measures in single- and multi-classifier systems alike, and we present a quantitative rather than intuitive perspective on the role of quality measures in classification. We notice the fact that for a given set of classification features and their fixed marginal distributions, the class separation in the joint feature space changes with the statistical dependencies observed between the individual features. The same effect applies to a feature space in which some of the features are class-independent. Consequently, we demonstrate that the class separation can be improved by augmenting the feature space with class-independent quality information, provided that it sports statistical dependencies on the class-selective features. We discuss how to construct classifier-quality measure ensembles in which the dependence between classification scores and the quality features helps decrease classification errors below those obtained using the classification scores alone. We propose Q – stack, a novel theoretical framework of improving classification with class-independent quality measures based on the concept of classifier stacking. In the scheme of Q – stack a classifier ensemble is used in which the first classifier layer is made of the baseline unimodal classifiers, and the second, stacked classifier operates on features composed of the normalized similarity scores and the relevant quality measures. We present Q – stack as a generalized framework of classification with quality information and we argue that previously proposed methods of classification with quality measures are its special cases. Further in this thesis we address the problem of estimating probability of single classification errors. We propose to employ the subjective Bayesian interpretation of single event probability as credence in the correctness of single classification decisions. We propose to apply the credence-based error predictor as a functional extension of the proposed Q – stack framework, where a Bayesian stacked classifier is employed. As such, the proposed method of credence estimation and error prediction inherits the benefit of seamless incorporation of quality information in the process of credence estimation. We propose a set of objective evaluation criteria for credence estimates, and we discuss how the proposed method can be applied together with an appropriate repair strategy to reduce classification errors to a desired target level. Finally, we demonstrate the application of Q – stack and its functional extension to single error prediction on the task of biometric identity verification using face and fingerprint modalities, and their multimodal combinations, using a real biometric database. We show that the use of the classification and error prediction methods proposed in this thesis allows for a systematic reduction of the error rates below those of the baseline classifiers