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 identification's 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.

In this paper we advocate a new technique for the fast identification of physical objects based on their physical unclonable features (surface microstructures). The proposed identification method is based on soft fingerprinting and consists of two stages: at the first stage the list of possible candidates is estimated based on the most reliable bits of a soft fingerprint and the traditional maximum likelihood decoding is applied to the obtained list to find a single best match at the second stage. The soft fingerprint is computed based on random projections with a sign-magnitude decomposition of projected coefficients. The estimate of a bit reliability is deduced directly from the observed coefficients. We investigate different decoding strategies to estimate the list of candidates, which minimize the probability of miss of the right index on the list. The obtained results show the flexibility of the proposed identification method to provide the performance-complexity trade-off.

This paper is dedicated to the analysis of object identification under desynchronization distortions. While this class of degradations is almost unavoidable in the functionality of such systems especially at the verification stage via acquisition imperfections, currently available their performance limits do not take its impact into consideration. In this paper we will try to close this gap providing the estimation of the achievable identification rates due to desynchronization distortions. Finally, the impact of the codeword length on the identification performance is justified.

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.

We study the problem of multiple hypothesis testing (HT) in view of a rejection option. That model of HT has many different applications. Errors in testing of M hypotheses regarding the source distribution with an option of rejecting all those hypotheses are considered. The source is discrete and arbitrarily varying (AVS). The tradeoffs among error probability exponents/reliabilities associated with false acceptance of rejection decision and false rejection of true distribution are investigated, the optimal decision strategies are outlined. The special case of discrete memoryless source (DMS) is also discussed. An interesting insight that the analysis implies is the phenomenon (comprehensible in terms of supervised/unsupervised learning) that in optimal discrimination within M hypothetical distributions one permits always lower error than in deciding to decline the set of hypotheses. Geometric interpretations of the optimal decision schemes and bounds in multi-HT for AVS's are given.

In this paper we analyze the problem of object identification in channels with desynchronization. In our analysis we assume that the identification system is designed using a pilot-based re-synchronization mechanism that assists desynchronization compensation with a certain accuracy. We demonstrate how the accuracy of re-synchronization impacts the information-theoretic limits of identification system performance.

Content identification based on digital fingerprinting attracts a lot of attention in different emerging applications. In this paper, we consider digital identification based on the sign-magnitude decomposition of fingerprint codewords and analyze the achievable rates for each component. We introduce a channel splitting approach and reveal certain interesting phenomena related to channel polarization. It is demonstrated that under certain conditions almost all rate in the sign channel is concentrated in reliable components, this can be of interest for complexity and security in various content identification applications. The envisioned extensions cover applications where the input and output alphabets of the channel are different at the encoding and decoding stages. Additionally, the reduction of the input data dimensionality at the encoding/enrollment stage can increase the cryptographic protection in terms of privacy leakage and simplify the decoding algorithms in biometric applications.

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.

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 Forney's erasure/list decoding. Along this analysis, we also consider complexity issues of fast content identification in large databases on remote untrusted servers.

In this paper, the performance of the content identification based on digital fingerprinting and order statistic list decoding is analyzed by evaluating the probabilities of correct identification, false acceptance and the probability mass function of queried binary fingerprint position on the list of candidates. The particular attention is dedicated to the cases when traditional maximum likelihood decoder fails to produce the reliable content identification. The maximum likelihood decoding is shown to be a particular case of order statistic list decoding for the list size equals 1. We demonstrate the efficiency of the proposed content identification system performance by investigating the probability mass function behavior and imposing the constraint on the cardinality of list size.

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 it is assumed that the codewords are of infinite length and that a jointly typical decoder is used in the analysis. These assumptions represent a certain over-generalization for the majority of practical applications. Consequently, the impact of the finite length on the mentioned limits remains an open and largely unexplored problem. Furthermore, leaking of privacy-related information to third parties due to storage, distribution and sharing of fingerprinting data represents an emerging research issue that should be addressed carefully. This paper contains an information-theoretic analysis of finite length digital fingerprinting under privacy constraints. A particular link between the considered setup and Forney's erasure/list decoding [1] is presented. Finally, complexity issues of reliable identification in large databases are addressed.

In this paper we consider the problem of reversible information hiding in the case when the attacker uses only discrete memoryless channels (DMC), the decoder knows only the class of channels, but not the DMC chosen by the attacker, the attacker knows the information-hiding strategy, probability distributions of all random variables, but not the side information. We introduce the notion of reversible information hiding Incapacity, which expresses the dependence of the information hiding rate on the error probability exponent E and the distortion levels for the information hider, for the attacker and for the host data approximation. The random coding bound for reversible information hiding B-capacity is found. We obtain the lower bound for reversibility information hiding capacity for E rarr 0. In particular, we have analyzed two special cases of the general problem formulation, pure reversibility and pure message communications.

Performance limits of multimodal detection systems are analyzed in this paper. Two main setups are considered, i.e., based on fusion of dependent and independent modalities, respectively. The analysis is performed in terms of attainable probability of detection errors characterized by the corresponding error exponents. It is demonstrated that an expected performance gain from fusion of dependent modalities is superior than in the case when one fuses independent signals. In order to quantify the efficiency of dependent modality fusion versus the independent case, the problem analysis is performed in the Gaussian formulation.

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 class of source and geometric channel models. Finally, we consider the practical hash construction, which compromises computational complexity, robustness to geometrical transformations, lack of priors about source statistics and security requirements. The proposed hash is based on a binary hypothesis testing for randomly or semantically selected blocks or regions in sequences or images. We present the results of experimental validation of the developed concept that justifies the practical efficiency of the elaborated framework.

In this paper, we address the analysis of the Costa setup under channel uncertainty. Since the Costa setup was entirely considered under the Gaussian assumptions about host and channel statistics, we assume that the channel is an additive white Gaussian noise (AWGN) with unknown variance defined on some interval. First, we solve the problem of average achievable rate optimization assuming that the distribution of noise variances is known. Then, we withdraw the previous assumption and consider that the variance distribution is unknown. The corresponding criteria are formulated and the performance of the Costa under these criteria is demonstrated

In this paper, we consider the problem of optimal self-embedding Laplacian data hiding for the state-dependent channels. In particular, we propose to decompose the Laplacian data using the paradigm of parallel source splitting. Experimental validation confirms the efficiency of the proposed approach

In this paper, we deal with the design of high-rate multilevel 2-D bar codes for the print-and-scan channel. First, we introduce a framework for evaluating the performance limits of these codes by studying an intersymbol-interference (ISI)-free, synchronous, and noiseless print-and-scan channel, where the input and output alphabets are finite and the printer device uses halftoning to simulate multiple gray levels. Second, we present a new model for the print-and-scan channel specifically adapted to the problem of communications via multilevel 2-D bar codes. This model, inspired by our experimental work, assumes perfect synchronization and absence of ISI, but independence between the channel input and the noise is not assumed. We adapt the theory of multilevel coding with multistage decoding (MLC/MSD) to the print-and-scan channel. Finally, we present experimental results confirming the utility of our channel model, and showing that multilevel 2-D bar codes using MLC/MSD can reliably achieve the high-capacity storage requirements of many multimedia security and management applications

Spread spectrum (SS) or known-host-statistics technique has shown the best performance in terms of both rate of reliable communications and bit error probability at the low watermark-to-noise ratio (WNR) regime. These results were obtained assuming that the host data follows an independent and identically distributed (i.i.d.) Gaussian distribution. However, in some widely used in practical data-hiding transform domains (like wavelet or discrete cosine transform domains) the host statistics have strong non-Gaussian character. Motivated by this stochastic modeling mismatch between the used assumption and the real case, a new set-up of the SS-based data-hiding with Laplacian host is presented for performance enhancement in terms of both bit error probability and achievable rates in additive white Gaussian noise (AWGN) channels based on the parallel splitting of Laplacian source.

In the scope of quantization-based watermarking techniques and additive attacks, there exists a common belief that the worst case attack (WCA) is given by additive white Gaussian noise (AWGN). Nevertheless, it has not been proved that the AWGN is indeed the WCA within the class of additive attacks against quantization-based watermarking. In this paper, the analysis of the WCA is theoretically developed with probability of error as a cost function. The adopted approach includes the possibility of masking the attack by a target probability density function (PDF) in order to trick smart decoding. The developed attack upper bounds the probability of error for quantization-based embedding schemes within the class of additive attacks.

In the paper we advocate image compression technique in the scope of distributed source coding framework. The novelty of the proposed approach is twofold: classical image compression is considered from the positions of source coding with side information and, contrarily to the existing scenarios, where side information is given explicitly, side information is created based on deterministic approximation of local image features. We consider an image in the transform domain as a realization of a source with a bounded codebook of symbols where each symbol represents a particular edge shape. The codebook is image independent and plays the role of auxiliary source. Due to the partial availability of side information at both encoder and decoder we treat our problem as a modification of Berger-Flynn-Gray problem and investigate a possible gain over the solutions when side information is either unavailable or available only at decoder. Finally, we present a practical compression algorithm for passport photo images based on our concept that demonstrates the superior performance in very low bit rate regime.