Oleksiy J. Koval

Multilevel 2-D Bar Codes: Toward High-Capacity Storage Modules for Multimedia Security and Management

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

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.

Multilevel 2D bar codes: toward high-capacity storage modules for multimedia security and management

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

Conception and limits of robust perceptual hashing: towards side information assisted hash functions

In this paper, we consider some basic concepts behind the design of existing robust perceptual hashing techniques for content identification. We show the limits of robust hashing from the communication perspectives as well as propose an approach that is able to overcome these shortcomings in certain setups. The consideration is based on both achievable rate and probability of error. We use the fact that most robust hashing algorithms are based on dimensionality reduction using random projections and quantization. Therefore, we demonstrate the corresponding achievable rate and probability of error based on random projections and compare with the results for the direct domain. The effect of dimensionality reduction is studied and the corresponding approximations are provided based on the Johnson-Lindenstrauss lemma. Side-information assisted robust perceptual hashing is proposed as a solution to the above shortcomings.

Security analysis of robust perceptual hashing

In this paper we considered the problem of security analysis of robust perceptual hashing in authentication application. The main goal of our analysis was to estimate the amount of trial efforts of the attacker, who is acting within the Kerckhoffs security principle, to reveal a secret key. For this purpose, we proposed to use Shannon equivocation that provides an estimate of complexity of the key search performed based on all available prior information and presented its application to security evaluation of particular robust perceptual hashing algorithms.

Robust perceptual hashing as classification problem: decision-theoretic and practical considerations

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.

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.

On security threats for robust perceptual hashing

Perceptual hashing has to deal with the constraints of robustness, accuracy and security. After modeling the process of hash extraction and the properties involved in this process, two different security threats are studied, namely the disclosure of the secret feature space and the tampering of the hash. Two different approaches for performing robust hashing are presented: Random-Based Hash (RBH) where the security is achieved using a random projection matrix and Content-Based Hash (CBH) were the security relies on the difficulty to tamper the hash. As for digital watermarking, different security setups are also devised: the Batch Hash Attack, the Group Hash Attack, the Unique Hash Attack and the Sensitivity Attack. A theoretical analysis of the information leakage in the context of Random-Based Hash is proposed. Finally, practical attacks are presented: (1) Minor Component Analysis is used to estimate the secret projection of Random-Based Hashes and (2) Salient point tampering is used to tamper the hash of Content-Based Hashes systems.

Towards reproducible results in authentication based on physical non-cloneable functions: The forensic authentication microstructure optical set (FAMOS)

Nowadays, the field of physical object security based on surface microstructures lacks common and shared data for the development, testing and fair benchmarking of new identification and authentication technologies. To our knowledge, most published results are based on proprietary data that also often lacks the necessary size for statistically significant results and conclusions. Therefore, in this paper, we introduce the first publicly available documented database for the investigation of physical object authentication based on non-cloneable surface microstructure images. We have built an automatic system suitable for massive acquisition of microstructure images from flat surfaces under different light conditions and with different cameras. The samples are acquired several times, and resulting images are aligned, labelled and online available to the public for further investigation and benchmarking of new methods. In this paper, we present the statistical properties for the images originating from 5000 unique carton packages acquired 6 times each with two different cameras. Furthermore, we derive statistical authentication frameworks for the original, the random projected and binarized domains presented together with all empirical results.

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.