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In this paper we propose an architecture for message-privacy preserving copy detection and content identification for images based on the signs of the Discrete Cosine Transform (DCT) coefficients. The architecture allows for searching in encrypted data and places the computational burden on the server. Sign components of the low frequency DCT coefficients of an image are used to generate a dual set...
SIFT descriptors are broadly used in various emerging applications. In recent years, these descriptors were deployed in compressed and binarized forms due to the computational complexity, storage, security and privacy cost incurred by working on real data. At the same time, the theoretical analysis of SIFT feature performance in different applications remains an open issue due to the lack of accurate...
In many problems such as biometrics, multimedia search, retrieval, recommendation systems requiring privacy-preserving similarity computations and identification, some binary features are stored in the public domain or outsourced to third parties that might raise certain privacy concerns about the original data. To avoid this privacy leak, privacy amplification is used. In the most cases, the privacy...
In light of the recent development of multimedia and networking technologies, an exponentially increasing amount of content is available via various public services. That is why content identification attracts a lot of attention. One possible technology for content identification is based on digital fingerprinting. When trying to establish information-theoretic limits in this application, usually...
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...
In this paper, we consider security-privacy issues in authentication techniques based on the extraction of common randomness. We demonstrate that the key rate-privacy leak pairs can be enhanced using reliable components extraction from specially designed random projections. The decrease of bit error probability is estimated and its impact on the key rate and privacy leak is evaluated. Several authentication...
In this work a novel fast search algorithm is proposed that is designed to offer improved performance in terms of identification accuracy whilst maintaining acceptable speed for forensic applications involving biometrics and Physically Unclonable Functions. A framework for forensic applications is presented, followed by a review of optimal and existing fast algorithms. We show why the new algorithm...
Using multiple binary classifiers is a popular way to construct multi-class classifiers. There exist several strategies to construct multi-class classifiers from binary classifiers. An important question is which strategy offers the highest probability of successful classification given the number of N binary classifiers used. The first result presented in this work is a method to approximate how...
In this paper, we consider the multiclass classification problem based on independent set of binary classifiers. Each binary classifier represents the output of quantized projection of training data onto a randomly generated orthonormal basis vector thus producing a binary label. The ensemble of all binary labels forms an analogue of a coding matrix. The properties of such kind of matrices and their...
In this paper, we consider robust hashing based on a bit reliability function that allows to enhance the performance in terms of both average probability of error and identification complexity. The obtained results demonstrate the high efficiency of the prosed approach.
In this paper we consider the problem of robust perceptual hashing as composite hypothesis testing. First, we formulate this problem as multiple hypothesis testing under prior ambiguity about source statistics and channel parameters representing a family of restricted geometric attacks. We introduce an efficient universal test that achieves the performance of informed decision rules for the specified...
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