Farzad Farhadzadeh

Information-theoretical analysis of private content identification

In recent years, content identification based on digital fingerprinting attracts a lot of attention in different emerging applications. At the same time, the theoretical analysis of digital fingerprinting systems for finite length case remains an open issue. Additionally, privacy leaks caused by fingerprint storage, distribution and sharing in a public domain via third party outsourced services cause certain concerns in the cryptographic community. In this paper, we perform an information-theoretic analysis of finite length digital fingerprinting systems in a private content identification setup and reveal certain connections between fingerprint based content identification and Forneys erasure/list decoding [1]. Along this analysis, we also consider complexity issues of fast content identification in large databases on remote untrusted servers.

Performance Analysis of Content-Based Identification Using Constrained List-Based Decoding

This paper is dedicated to the performance analysis of content-based identification using binary fingerprints and constrained list-based decoding. We formulate content-based identification as a multiple hypothesis test and develop analytical models of its performance in terms of probabilities of correct detection/miss and false acceptance for a class of statistical models, which captures the correlation between elements of either the content or its extracted features. Furthermore, in order to determine the block/codeword length impact on the identifications accuracy, we analyze exponents of these probabilities of errors. Finally, we develop a probabilistic model, justifying the accuracy of identification based on list decoding by evaluating the position of the queried entry on the output list. The obtained results make it possible to characterize the performance of traditional unique decoding, based on the maximum likelihood for the situations when the decoder fails to produce the correct index. This paper also contains experimental results that confirm theoretical findings.

Active content fingerprinting: A marriage of digital watermarking and content fingerprinting

Content fingerprinting and digital watermarking are techniques that are used for the content protection and distribution monitoring. Nowadays, both techniques are well studied and their shortcomings are understood. In this paper, we introduce a new framework named as active content fingerprinting that takes the best from two worlds of content fingerprinting and digital watermarking to overcome some of fundamental restrictions of these techniques in terms of performance and complexity. The proposed framework extends the encoding of conventional content fingerprinting in the way similar to digital watermarking thus allowing to extract the fingerprints from the modified cover data. We consider several encoding strategies, examine the performance of the proposed schemes in terms of bit error rate and compare it with those of conventional fingerprinting and digital watermarking.

Content authentication and identification under informed attacks

We consider the problem of content identification and authentication based on digital content fingerprinting. Contrary to existing work in which the performance of these systems under blind attacks is analysed, we investigate the information-theoretic performance under informed attacks. In the case of binary content fingerprinting, in a blind attack, a probe is produced at random independently from the fingerprints of the original contents. Contrarily, informed attacks assume that the attacker might have some information about the original content and is thus able to produce a counterfeit probe that is related to an authentic fingerprint corresponding to an original item, thus leading to an increased probability of false acceptance. We demonstrate the impact of the ability of an attacker to create counterfeit items whose fingerprints are related to fingerprints of authentic items, and consider the influence of the length of the fingerprint on the performance of finite-length systems. Finally, the information-theoretic achieveble rate of content identification systems sustaining informed attacks is derived under asymptotic assumptions about the fingerprint length.

Active content fingerprinting: Shrinkage and lattice based modulations

In this paper, we extend a new framework introduced as active content fingerprinting in [1] 1 that takes the best from the two worlds of content fingerprinting and digital watermarking to overcome some of the fundamental restrictions of these techniques in terms of performance and complexity. In the proposed framework, contents are modified in a way similar to watermarking to extract more robust fingerprints in contrast to conventional content fingerprinting. We investigate the performance of two modulation techniques based on unidimensional shrinkage and multidimensional lattice quantization. The simulation results on real images demonstrate the high efficiency of the proposed methods facing low-quality compression and additive noise.

Performance analysis of identification system based on order statistics list decoder

In this work we advocate an approach for the statistical performance analysis of an identification system. The statistical performance analysis is accomplished for the corresponding probability of miss and false acceptance based on the order statistic list decoding framework.

Fundamental limits of identification: Identification rate, search and memory complexity trade-off

In this paper, we introduce a new generalized scheme to resolve the trade-off between the identification rate, search and memory complexities in large-scale identification systems. The main contribution of this paper consists in a special database organization based on assigning entries of a database to a set of predefined and possibly overlapping clusters, where the cluster representative points are generated based on statistics of both entries of the database and queries. The decoding procedure is accomplished in two stages: At the first stage, a list of clusters related to the query is estimated, then refinement checks are performed to all members of these clusters to produce a unique index at the second stage. The proposed scheme generalizes several practical searching in identification systems as well as makes it possible to approach a new achievable region of search- memory complexity trade-off.

On accuracy, robustness, and security of bag-of-word search systems

In this paper, we present a statistical framework for the analysis of the performance of Bag-of-Words (BOW) systems. The paper aims at establishing a better understanding of the impact of different elements of BOW systems such as the robustness of descriptors, accuracy of assignment, descriptor compression and pooling and finally decision making. We also study the impact of geometrical information on the BOW system performance and compare the results with different pooling strategies. The proposed framework can also be of interest for a security and privacy analysis of BOW systems. The experimental results on real images and descriptors confirm our theoretical findings. Notation: We use capital letters to denote scalar random variables X and X to denote vector random variables, corresponding small letters x and x to denote the realisations of scalar and vector random variables, respectively. We use X ~pX(x) or simply X ~p(x) to indicate that a random variable X is distributed according to pX(x). N(μ, σ 2 X ) stands for the Gaussian distribution with mean μ and variance σ2 X . B(L, Pb) denotes the binomial distribution with sequence length L and probability of success Pb. ║.║denotes the Euclidean vector norm and Q(.) stands for the Q-function. D(.║.) denotes the divergence and E{.} denotes the expectation.

Active Content Fingerpriting

Content fingerprinting and digital watermarking are techniques that are used for content protection and distribution monitoring and, more recently, for interaction with physical objects. Over the past few years, both techniques have been well studied and their shortcomings understood. In this paper, we introduce a new framework called active content fingerprinting, which takes the best from two worlds of content fingerprinting and digital watermarking, in order to overcome some of the fundamental restrictions of these techniques in terms of performance and complexity. The proposed framework extends the encoding process of conventional content fingerprinting in a way similar to digital watermarking, thus allowing the extraction of fingerprints from the modified cover data. We consider several encoding strategies, examine the performance of the proposed schemes in terms of bit error rate, the probabilities of correct identification and false acceptance and compare it with those of conventional fingerprinting and digital watermarking. Finally, we extend the proposed framework to the multidimensional case based on lattices and demonstrate its performance on both synthetic data and real images.

Information-theoretic analysis of content based identification for correlated data

A number of different multimedia fingerprinting algorithms and identification techniques were proposed and analyzed recently. This paper presents a content identification setup for a class of multimedia data that can be modeled by the Gauss-Markov process. We advocate a constrained order statistics decoding scheme based on digital fingerprints extracted from correlated data to identify contents. Finally, we investigate the fundamental limits of the proposed setup by deriving bounds on the miss and false acceptance probabilities.