Theoretical Foundations of Adversarial Detection and Applications to Multimedia Forensics

Every day we share our personal information with digital systems which are constantly exposed to threats. Security-oriented disciplines of signal processing have then received increasing attention in the last decades: multimedia forensics, digital watermarking, biometrics, network intrusion detection, steganography and steganalysis are just a few examples. Even though each of these fields has its own peculiarities, they all have to deal with a common problem: the presence of adversaries aiming at making the system fail. It is the purpose of Adversarial Signal Processing to lay the basis of a general theory that takes into account the impact of an adversary on the design of effective signal processing tools. By focusing on the most prominent problem of Adversarial Signal Processing, namely binary detection or Hypothesis Testing, we contribute to the above mission with a general theoretical framework for the binary detection ...

Tondi, Benedetta — University of Siena


Decentralized Estimation Under Communication Constraints

In this thesis, we consider the problem of decentralized estimation under communication constraints in the context of Collaborative Signal and Information Processing. Motivated by sensor network applications, a high volume of data collected at distinct locations and possibly in diverse modalities together with the spatially distributed nature and the resource limitations of the underlying system are of concern. Designing processing schemes which match the constraints imposed by the system while providing a reasonable accuracy has been a major challenge in which we are particularly interested in the tradeoff between the estimation performance and the utilization of communications subject to energy and bandwidth constraints. One remarkable approach for decentralized inference in sensor networks is to exploit graphical models together with message passing algorithms. In this framework, after the so-called information graph of the problem is constructed, it is mapped onto the ...

Uney, Murat — Middle East Technical University


Optimization of Positioning Capabilities in Wireless Sensor Networks: from power efficiency to medium access

In Wireless Sensor Networks (WSN), the ability of sensor nodes to know its position is an enabler for a wide variety of applications for monitoring, control, and automation. Often, sensor data is meaningful only if its position can be determined. Many WSN are deployed indoors or in areas where Global Navigation Satellite System (GNSS) signal coverage is not available, and thus GNSS positioning cannot be guaranteed. In these scenarios, WSN may be relied upon to achieve a satisfactory degree of positioning accuracy. Typically, batteries power sensor nodes in WSN. These batteries are costly to replace. Therefore, power consumption is an important aspect, being performance and lifetime ofWSN strongly relying on the ability to reduce it. It is crucial to design effective strategies to maximize battery lifetime. Optimization of power consumption can be made at different layers. For example, at the ...

Moragrega, Ana — Universitat Politecnica de Catalunya


Convergence Analysis of Distributed Consensus Algorithms

Inspired by new emerging technologies and networks of devices with high collective computational power, I focus my work on the problematics of distributed algorithms. While each device runs a relatively simple algorithm with low complexity, the group of interconnected units (agents) determines a behavior of high complexity. Typically, such units have their own memory and processing unit, and are interconnected and capable to exchange information with each other. More specifically, this work is focused on the distributed consensus algorithms. Such algorithms allow the agents to coordinate their behaviour and to distributively find a common agreement (consensus). To understand and analyze their behaviour, it is necessary to analyze the convergence of the consensus algorithm, i.e., under which conditions the algorithm reaches a consensus and under which it does not. Naturally, the communication channel can change and the agents may function asynchronously ...

Sluciak, Ondrej — Vienna University of Technology


Statistical Signal Processing for Data Fusion

In this dissertation we focus on statistical signal processing for Data Fusion, with a particular focus on wireless sensor networks. Six topics are studied: (i) Data Fusion for classification under model uncertainty; (ii) Decision Fusion over coherent MIMO channels; (iii) Performance analysis of Maximum Ratio Combining in MIMO decision fusion; (iv) Decision Fusion over non-coherent MIMO channels; (v) Decision Fusion for distributed classification of multiple targets; (vi) Data Fusion for inverse localization problems, with application to wideband passive sonar platform estimation. The first topic of this thesis addresses the problem of lack of knowledge of the prior distribution in classification problems that operate on small data sets that may make the application of Bayes' rule questionable. Uniform or arbitrary priors may provide classification answers that, even in simple examples, may end up contradicting our common sense about the problem. Entropic ...

Ciuonzo, Domenico — Second University of Naples


Robust Game-Theoretic Algorithms for Distributed Resource Allocation in Wireless Communications

The predominant game-theoretic solutions for distributed rate-maximization algorithms in Gaussian interference channels through optimal power control require perfect channel knowledge, which is not possible in practice due to various reasons, such as estimation errors, feedback quantization and latency between channel estimation and signal transmission. This thesis therefore aims at addressing this issue through the design and analysis of robust game-theoretic algorithms for rate-maximization in Gaussian interference channels in the presence of bounded channel uncertainty. A robust rate-maximization game is formulated for the single-antenna frequency-selective Gaussian interference channel under bounded channel uncertainty. The robust-optimization equilibrium solution for this game is independent of the probability distribution of the channel uncertainty. The existence and uniqueness of the equilibrium are studied and sufficient conditions for the uniqueness of the equilibrium are provided. Distributed algorithms to compute the equilibrium solution are presented and shown to ...

Anandkumar, Amod Jai Ganesh — Loughborough University


Decentralized Parameter and Random Field Estimation with Wireless Sensor Netwoks

In recent years, research on Wireless Sensor Networks (WSN) has attracted considerable attention. This is in part motivated by the large number of applications in which WSNs are called to play a pivotal role, such as parameter estimation (namely, moisture, temperature), event detection (leakage of pollutants, earthquakes, fires), or localization and tracking (for e.g. border control, inventory tracking), to name a few. This PhD dissertation is focused on the design of decentralized estimation schemes for wireless sensor networks. In this context, sensors observe a given phenomenon of interest (e.g. temperature). Consequently, sensor observations are conveyed over the wireless medium to a Fusion Center (FC) for further processing. The ultimate goal of the WSN is the estimation or reconstruction of the phenomenon with minimum distortion. The problem is addressed from a signal processing and information-theoretical perspective. However, the interplay with some ...

Javier Matamoros Morcillo — Centre Tecnològic de Telecomuniacions de Catalunya (CTTC)


Security/Privacy Analysis of Biometric Hashing and Template Protection for Fingerprint Minutiae

This thesis has two main parts. The first part deals with security and privacy analysis of biometric hashing. The second part introduces a method for fixed-length feature vector extraction and hash generation from fingerprint minutiae. The upsurge of interest in biometric systems has led to development of biometric template protection methods in order to overcome security and privacy problems. Biometric hashing produces a secure binary template by combining a personal secret key and the biometric of a person, which leads to a two factor authentication method. This dissertation analyzes biometric hashing both from a theoretical point of view and in regards to its practical application. For theoretical evaluation of biohashes, a systematic approach which uses estimated entropy based on degree of freedom of a binomial distribution is outlined. In addition, novel practical security and privacy attacks against face image hashing ...

Berkay Topcu — Sabanci University


Robust Signal Processing in Distributed Sensor Networks

Statistical robustness and collaborative inference in a distributed sensor network are two challenging requirements posed on many modern signal processing applications. This dissertation aims at solving these tasks jointly by providing generic algorithms that are applicable to a wide variety of real-world problems. The first part of the thesis is concerned with sequential detection---a branch of detection theory that is focused on decision-making based on as few measurements as possible. After reviewing some fundamental concepts of statistical hypothesis testing, a general formulation of the Consensus+Innovations Sequential Probability Ratio Test for sequential binary hypothesis testing in distributed networks is derived. In a next step, multiple robust versions of the algorithm based on two different robustification paradigms are developed. The functionality of the proposed detectors is verified in simulations, and their performance is examined under different network conditions and outlier concentrations. Subsequently, ...

Leonard, Mark Ryan — Technische Universität Darmstadt


Probabilistic modeling for sensor fusion with inertial measurements

In recent years, inertial sensors have undergone major developments. The quality of their measurements has improved while their cost has decreased, leading to an increase in availability. They can be found in stand-alone sensor units, so-called inertial measurement units, but are nowadays also present in for instance any modern smartphone, in Wii controllers and in virtual reality headsets. The term inertial sensor refers to the combination of accelerometers and gyroscopes. These measure the external specific force and the angular velocity, respectively. Integration of their measurements provides information about the sensor’s position and orientation. However, the position and orientation estimates obtained by simple integration suffer from drift and are therefore only accurate on a short time scale. In order to improve these estimates, we combine the inertial sensors with additional sensors and models. To combine these different sources of information, also ...

Kok, Manon — Linköping University


Distributed Signal Processing Algorithms for Multi-Task Wireless Acoustic Sensor Networks

Recent technological advances in analogue and digital electronics as well as in hardware miniaturization have taken wireless sensing devices to another level by introducing low-power communication protocols, improved digital signal processing capabilities and compact sensors. When these devices perform a certain pre-defined signal processing task such as the estimation or detection of phenomena of interest, a cooperative scheme through wireless connections can significantly enhance the overall performance, especially in adverse conditions. The resulting network consisting of such connected devices (or nodes) is referred to as a wireless sensor network (WSN). In acoustical applications (e.g., speech enhancement) a variant of WSNs, called wireless acoustic sensor networks (WASNs) can be employed in which the sensing unit at each node consists of a single microphone or a microphone array. The nodes of such a WASN can then cooperate to perform a multi-channel acoustic ...

Hassani, Amin — KU Leuven


Parametric and non-parametric approaches for multisensor data fusion

Multisensor data fusion technology combines data and information from multiple sensors to achieve improved accuracies and better inference about the environment than could be achieved by the use of a single sensor alone. In this dissertation, we propose parametric and nonparametric multisensor data fusion algorithms with a broad range of applications. Image registration is a vital first step in fusing sensor data. Among the wide range of registration techniques that have been developed for various applications, mutual information based registration algorithms have been accepted as one of the most accurate and robust methods. Inspired by the mutual information based approaches, we propose to use the joint R´enyi entropy as the dissimilarity metric between images. Since the R´enyi entropy of an image can be estimated with the length of the minimum spanning tree over the corresponding graph, the proposed information-theoretic registration ...

Ma, Bing — University of Michigan


Privacy protection preserving the utility of visual surveillance

Due to some tragic events such as crime, bank robberies and terrorist attacks, an unparalleled surge in video surveillance cameras has occurred in recent years. In consequence, our daily life is overseen everywhere (e.g. on the street, in stations, in shops and in the workplace). For example, on average, people living in London can be caught on cameras more than 300 times a day. At the same time, automatic processing technology and quality of sensors have advanced significantly, which has even enabled automatic detection, tracking and identification of individuals. With the proliferation of video surveillance systems and the progress in automatic recognition, privacy protection is now becoming a significant concern. Video surveillance is intrusive because it allows the observation of certain information that is considered as private (i.e., identity or some characteristics such as age, race, gender). Nowadays, some processing ...

Ruchaud, Natacha — Eurecom


ON THE PERFORMANCE OF HELPER DATA

The use of biometrics looks promising as it is already being applied in electronic passports, ePassports, on a global scale. Because the biometric data has to be stored as a reference template on either a central or personal storage device, its wide-spread use introduces new security and privacy risks such as (i) identity fraud, (ii) cross-matching, (iii) irrevocability and (iv) leaking sensitive medical information. Mitigating these risks is essential to obtain the acceptance from the subjects of the biometric systems and therefore facilitating the successful implementation on a large-scale basis. A solution to mitigate these risks is to use template protection techniques. The required protection properties of the stored reference template according to ISO guidelines are (i) irreversibility, (ii) renewability and (iii) unlinkability. A known template protection scheme is the helper data system (HDS). The fundamental principle of the HDS ...

Kelkboom, Emile — University of Twente


Reconstruction and clustering with graph optimization and priors on gene networks and images

The discovery of novel gene regulatory processes improves the understanding of cell phenotypic responses to external stimuli for many biological applications, such as medicine, environment or biotechnologies. To this purpose, transcriptomic data are generated and analyzed from DNA microarrays or more recently RNAseq experiments. They consist in genetic expression level sequences obtained for all genes of a studied organism placed in different living conditions. From these data, gene regulation mechanisms can be recovered by revealing topological links encoded in graphs. In regulatory graphs, nodes correspond to genes. A link between two nodes is identified if a regulation relationship exists between the two corresponding genes. Such networks are called Gene Regulatory Networks (GRNs). Their construction as well as their analysis remain challenging despite the large number of available inference methods. In this thesis, we propose to address this network inference problem ...

Pirayre, Aurélie — IFP Energies nouvelles

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