Anti-Collusion Fingerprinting for Multimedia

Digital fingerprinting is a technique for identifyingusers who might try to use multimedia content for unintendedpurposes, such as redistribution. These fingerprints are typicallyembedded into the content using watermarking techniques that aredesigned to be robust to a variety of attacks. A cost-effectiveattack against such digital fingerprints is collusion, whereseveral differently marked copies of the same content are combinedto disrupt the underlying fingerprints. In this paper, weinvestigate the problem of designing fingerprints that canwithstand collusion and allow for the identification of colluders.We begin by introducing the collusion problem for additiveembedding. We then study the effect that averaging collusion hasupon orthogonal modulation. We introduce an efficient detectionalgorithm for identifying the fingerprints associated with Kcolluders that requires O(K log(n/K)) correlations for agroup of n users. We next develop a fingerprinting scheme basedupon code modulation that does not require as many basis signalsas orthogonal modulation. We propose a new class of codes, calledanti-collusion codes (ACC), which have the property that thecomposition of any subset of K or fewer codevectors is unique.Using this property, we can therefore identify groups of K orfewer colluders. We present a construction of binary-valued ACCunder the logical AND operation that uses the theory ofcombinatorial designs and is suitable for both the on-off keyingand antipodal form of binary code modulation. In order toaccommodate n users, our code construction requires onlyO(sqrt{n}) orthogonal signals for a given number of colluders.We introduce four different detection strategies that can be usedwith our ACC for identifying a suspect set of colluders. Wedemonstrate the performance of our ACC for fingerprintingmultimedia and identifying colluders through experiments usingGaussian signals and real images.This paper has been submitted to IEEE Transactions on Signal Processing</I

Random Codes and Graphs for Secure Communication

This dissertation considers two groups of problems related to secure communication. The first line of research is devoted to theoretical problems of copyright protection of digital content. Embedding identification data in the content is a well-developed technique of content protection known under the name of fingerprinting. Schemes that provide such protection are known as fingerprinting codes in the literature. We study limits of the number of users of a fingerprinting system as well as constructions of low-complexity fingerprinting codes that support a large number of users. The second problem that is addressed in the dissertation relates to connectivity analysis of ad hoc wireless networks. One of the basic requirements in such environments is to ensure that none of the nodes are completely isolated from the network. We address the problem of characterizing threshold parameters for node isolation that enable the system designer to choose the power needed for network operation based on the outage probability of links in the network. The methods of this research draw from coding theory, information theory and random graphs. An idea that permeates most results in this dissertation is the application of randomization both in the analysis of fingerprinting and node isolation. The main contributions of this dissertation belong in the area of fingerprinting and are described as follows. We derive new lower and upper bounds on the optimal trade-off between the number of users and the length of the fingerprints required to ensure reliability of the system, which we call fingerprinting capacity. Information-theoretic techniques employed in our proofs of bounds on capacity originate in coding theorems for channels with multiple inputs. Constructions of fingerprinting codes draw on methods of coding theory related to list decoding and code concatenation. We also analyze random graph models for ad hoc networks with link failures and secure sensor networks that employ randomized key distribution. We establish a precise zero-one law for node isolation in the model with link failures for nodes placed on the circle. We further generalize this result to obtain a one-law for secure sensor networks on some surfaces

Digital watermarking and novel security devices

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Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

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

Investigation of Multimodal Template-Free Biometric Techniques and Associated Exception Handling

The Biometric systems are commonly used as a fundamental tool by both government and private sector organizations to allow restricted access to sensitive areas, to identify the criminals by the police and to authenticate the identification of individuals requesting to access to certain personal and confidential services. The applications of these identification tools have created issues of security and privacy relating to personal, commercial and government identities. Over the last decade, reports of increasing insecurity to the personal data of users in the public and commercial domain applications has prompted the development of more robust and sound measures to protect the personal data of users from being stolen and spoofing. The present study aimed to introduce the scheme for integrating direct and indirect biometric key generation schemes with the application of Shamir‘s secret sharing algorithm in order to address the two disadvantages: revocability of the biometric key and the exception handling of biometric modality. This study used two different approaches for key generation using Shamir‘s secret sharing scheme: template based approach for indirect key generation and template-free. The findings of this study demonstrated that the encryption key generated by the proposed system was not required to be stored in the database which prevented the attack on the privacy of the data of the individuals from the hackers. Interestingly, the proposed system was also able to generate multiple encryption keys with varying lengths. Furthermore, the results of this study also offered the flexibility of providing the multiple keys for different applications for each user. The results from this study, consequently, showed the considerable potential and prospect of the proposed scheme to generate encryption keys directly and indirectly from the biometric samples, which could enhance its success in biometric security field

Biometrics & [and] Security:Combining Fingerprints, Smart Cards and Cryptography

Since the beginning of this brand new century, and especially since the 2001 Sept 11 events in the U.S, several biometric technologies are considered mature enough to be a new tool for security. Generally associated to a personal device for privacy protection, biometric references are stored in secured electronic devices such as smart cards, and systems are using cryptographic tools to communicate with the smart card and securely exchange biometric data. After a general introduction about biometrics, smart cards and cryptography, a second part will introduce our work with fake finger attacks on fingerprint sensors and tests done with different materials. The third part will present our approach for a lightweight fingerprint recognition algorithm for smart cards. The fourth part will detail security protocols used in different applications such as Personal Identity Verification cards. We will discuss our implementation such as the one we developed for the NIST to be used in PIV smart cards. Finally, a fifth part will address Cryptography-Biometrics interaction. We will highlight the antagonism between Cryptography – determinism, stable data – and Biometrics – statistical, error-prone –. Then we will present our application of challenge-response protocol to biometric data for easing the fingerprint recognition process

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements