Fokko Beekhof

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.

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.

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.

Fast identification algorithms for forensic applications

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 has the power to outperform the other algorithms with a theoretic analysis and confirm this using simulations on a large database.

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.

Secure surface identification codes

This paper introduces an identification framework for random microstructures of material surfaces. These microstructures represent a kind of unique fingerprints that can be used to track and trace an item as well as for anti-counterfeiting. We first consider the architecture for mobile phone-based item identification and then introduce a practical identification algorithm enabling fast searching in large databases. The proposed algorithm is based on reference list decoding. The link to digital communications and robust perceptual hashing is shown. We consider a practical construction of reference list decoding, which comprizes computational complexity, security, memory storage and performance requirements. The efficiency of the proposed algorithm is demonstrated on experimental data obtained from natural paper surfaces.

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.

Fast physical object identification based on unclonable features and soft fingerprinting

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.