We present an innovative method for constructing ensembles of modular networks designed for data classification, based on novel one-class classifier-type structures equipped with a so-called glial driver. This concept is inspired by recent neurobiological discoveries highlighting the significant impact of glial cells on cognitive processes in the human brain. The proposed solution was implemented and validated under real-world conditions within a Polish government ministry to automate document routing. The process of building one-class classifier models is also novel, as it involves training a single structure with the entire training sequence before converting the trained structure into a one-class classifier model using glial cells. This approach demonstrates a substantial reduction in the training time of the modular system and a significant improvement in its performance.

OBJECTIVE: The primary objective of our study is to address the challenge of confidentially sharing medical images across different centers. This is often a critical necessity in both clinical and research environments, yet restrictions typically exist due to privacy concerns. Our aim is to design a privacy-preserving data-sharing mechanism that allows medical images to be stored as encoded and obfuscated representations in the public domain without revealing any useful or recoverable content from the images. In tandem, we aim to provide authorized users with compact private keys that could be used to reconstruct the corresponding images. METHOD: Our approach involves utilizing a neural auto-encoder. The convolutional filter outputs are passed through sparsifying transformations to produce multiple compact codes. Each code is responsible for reconstructing different attributes of the image. The key privacy-preserving element in this process is obfuscation through the use of specific pseudo-random noise. When applied to the codes, it becomes computationally infeasible for an attacker to guess the correct representation for all the codes, thereby preserving the privacy of the images. RESULTS: The proposed framework was implemented and evaluated using chest X-ray images for different medical image analysis tasks, including classification, segmentation, and texture analysis. Additionally, we thoroughly assessed the robustness of our method against various attacks using both supervised and unsupervised algorithms. CONCLUSION: This study provides a novel, optimized, and privacy-assured data-sharing mechanism for medical images, enabling multi-party sharing in a secure manner. While we have demonstrated its effectiveness with chest X-ray images, the mechanism can be utilized in other medical images modalities as well.

In this paper, we present a new approach to model a printing-imaging channel using a machine learning-based “digital twin” for copy detection patterns (CDP). The CDP are considered as modern anti-counterfeiting features in multiple applications. Our digital twin is formulated within the information-theoretic framework of TURBO initially developed for high energy physics simulations, using variational approximations of mutual information for both encoder and decoder in the bidirectional exchange of information. This model extends various architectural designs, including paired pix2pix and unpaired CycleGAN, for image-to-image translation. Applicable to any type of printing and imaging devices, the model needs only training data comprising digital templates sent to a printing device and data acquired by an imaging device. The data can be paired, unpaired, or hybrid, ensuring architectural flexibility and scalability for multiple practical setups. We explore the influence of various architectural factors, metrics, and discriminators on the overall system’s performance in generating and predicting printed CDP from their digital versions and vice versa. We also performed a comparison with several state-of-the-art methods for image-to-image translation applications. The simulation code and extended results are publicly available at https://gitlab.unige.ch/sip-group/digital-twin.

Copy Detection Pattern (CDP) technology is a promising anti-counterfeiting solution for the protection of physical goods. In recent years, it has been shown that this technology is threatened by powerful deep learning attacks that are able to bypass original authentication schemes. In this paper, we tackle this problem by proposing a new CDP authentication scheme based on statistical knowledge discovered about the printing and imaging process. The novelty of our approach lies in providing means to measure the reliability of each local pattern appearing in the CDP. This allows to define new authentication measures to better differentiate original CDP from fakes. Our results show that this new system is capable of performing reliable CDP authentication with smartphones without the need for heavyweight machine learning tools requiring massive data entries.

In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applications. However, the security of CDP in terms of their reproducibility by unauthorized parties or clonability remains largely unexplored. In this respect, this paper addresses a problem of anti-counterfeiting of physical objects and aims at investigating the authentication aspects and the resistances to illegal copying of the modern CDP from machine learning perspectives. A special attention is paid to a reliable authentication under the real-life verification conditions when the codes are printed on an industrial printer and enrolled via modern mobile phones under regular light conditions. The theoretical and empirical investigation of authentication aspects of CDP is performed with respect to four types of copy fakes from the point of view of (i) multi-class supervised classification as a baseline approach and (ii) one-class classification as a real-life application case. The obtained results show that the modern machine-learning approaches and the technical capacities of modern mobile phones allow to reliably authenticate CDP on end-user mobile phones under the considered classes of fakes.

Bottleneck problems are an important class of optimization problems that have recently gained increasing attention in the domain of machine learning and information theory. They are widely used in generative models, fair machine learning algorithms, design of privacy-assuring mechanisms, and appear as information-theoretic performance bounds in various multi-user communication problems. In this work, we propose a general family of optimization problems, termed ascomplexity-leakage-utility bottleneck (CLUB)model, which (i) provides a unified theoretical framework that generalizes most of the state-of-the-art literature for the information-theoretic privacy models, (ii) establishes a new interpretation of the popular generative and discriminative models, (iii) constructs new insights for the generative compression models, and (iv) can be used to obtain fair generative models. We first formulate the CLUB model as a complexity-constrained privacy-utility optimization problem. We then connect it with the closely related bottleneck problems, namely information bottleneck (IB), privacy funnel (PF), deterministic IB (DIB), conditional entropy bottleneck (CEB), and conditional PF (CPF). We show that the CLUB model generalizes all these problems as well as most other information-theoretic privacy models. Then, we construct the deep variational CLUB (DVCLUB) models by employing neural networks to parameterize variational approximations of the associated information quantities. Building upon these information quantities, we present unified objectives of thesupervisedandunsupervisedDVCLUB models. Leveraging the DVCLUB model in an unsupervised setup, we then connect it with state-of-the-art generative models, such as variational auto-encoders (VAEs), generative adversarial networks (GANs), as well as the Wasserstein GAN (WGAN), Wasserstein auto-encoder (WAE), and adversarial auto-encoder (AAE) models through the optimal transport (OT) problem. We then show that the DVCLUB model can also be used in fair representation learning problems, where the goal is to mitigate the undesired bias during the training phase of a machine learning model. We conduct extensive quantitative experiments on colored-MNIST and CelebA datasets.

In this paper, we consider privacy-preserving compressed image sharing, where the goal is to release compressed data whilst satisfying some privacy/secrecy constraints yet ensuring image reconstruction with a defined fidelity. The privacy-preserving compressed image sharing is addressed using a machine learning framework based on an information bottleneck with a shared secret key for authorized users. In contrast, an adversary observing the protected compressed representation tries to either reconstruct the data or deduce some privacy-sensitive attributes such as gender, age, etc. The inference task on the adversary’s side is performed without the knowledge of the shared secret key and is based on an adversarial mutual information maximization between the privacy-protected compressed representation and targeted attributes. The proposed framework is experimentally validated on the CelebA dataset.

Copy detection patterns (CDP) are an attractive technology that allows manufacturers to defend their products against counterfeiting. The main assumption behind the protection mechanism of CDP is that these codes printed with the smallest symbol size (1x1) on an industrial printer cannot be copied or cloned with sufficient accuracy due to data processing inequality. However, previous works have shown that Machine Learning (ML) based attacks can produce high-quality fakes, resulting in decreased accuracy of authentication based on traditional feature-based authentication systems. While Deep Learning (DL) can be used as a part of the authentication system, to the best of our knowledge, none of the previous works has studied the performance of a DL-based authentication system against ML-based attacks on CDP with 1x1 symbol size. In this work, we study such a performance assuming a supervised learning (SL) setting.

In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding. The core of the framework relies on sparse coding with ambiguation (SCA) mechanism that introduces the notion of inherent shared secrecy based on the support intersection of sparse codes. This approach is ‘fairness-aware’, in the sense that any point in the neighborhood has an equiprobable chance to be chosen. Our approach can be applied to raw data, latent representation of autoencoders, and aggregated local descriptors. The proposed method is tested on both synthetic i.i.d data and real image databases.

We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups. We argue that, while compactness is always desired at scale, this need is more severe when trying to furthermore protect the privacy-sensitive content. We therefore encode images, such that, from one hand, representations are stored in the public domain without paying the huge cost of privacy protection, but ambiguated and hence leaking no discernible content from the images, unless a combinatorially-expensive guessing mechanism is available for the attacker. From the other hand, authorized users are provided with very compact keys that can easily be kept secure. This can be used to disambiguate and reconstruct faithfully the corresponding access-granted images. We achieve this with a convolutional autoencoder of our design, where feature maps are passed independently through sparsifying transformations, providing multiple compact codes, each responsible for reconstructing different attributes of the image. The framework is tested on a large-scale database of images with public implementation available.

This paper addresses a problem of anti-counterfeiting of physical objects and aims at investigating a possibility of counterfeited printable graphical code detection from a machine learning perspectives. We investigate a fake generation via two different deep regeneration models and study the authentication capacity of several discriminators on the data set of real printed graphical codes where different printing and scanning qualities are taken into account. The obtained experimental results provide a new insight on scenarios, where the printable graphical codes can be accurately cloned and could not be distinguished.

In recent years, classification techniques based on deep neural networks (DNN) were widely used in many fields such as computer vision, natural language processing, and self-driving cars. However, the vulnerability of the DNN-based classification systems to adversarial attacks questions their usage in many critical applications. Therefore, the development of robust DNN-based classifiers is a critical point for the future deployment of these methods. Not less important issue is understanding of the mechanisms behind this vulnerability. Additionally, it is not completely clear how to link machine learning with cryptography to create an information advantage of the defender over the attacker. In this paper, we propose a key-based diversified aggregation (KDA) mechanism as a defense strategy in a gray- and black-box scenario. KDA assumes that the attacker (i) knows the architecture of classifier and the used defense strategy, (ii) has an access to the training data set, but (iii) does not know a secret key and does not have access to the internal states of the system. The robustness of the system is achieved by a specially designed key-based randomization. The proposed randomization prevents the gradients' back propagation and restricts the attacker to create a "bypass" system. The randomization is performed simultaneously in several channels. Each channel introduces its own randomization in a special transform domain. The sharing of a secret key between the training and test stages creates an information advantage to the defender. Finally, the aggregation of soft outputs from each channel stabilizes the results and increases the reliability of the final score. The performed experimental evaluation demonstrates a high robustness and universality of the KDA against state-of-the-art gradient-based gray-box transferability attacks and the non-gradient-based black-box attacks (The results reported in this paper have been partially presented in CVPR 2019 (Taran et al., Defending against adversarial attacks by randomized diversification, 2019) & ICIP 2019 (Taran et al., Robustification of deep net classifiers by key-based diversified aggregation with pre-filtering, 2019)).

The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications. In this paper, we propose a randomized diversification as a defense strategy. We introduce a multi-channel architecture in a gray-box scenario, which assumes that the architecture of the classifier and the training data set are known to the attacker. The attacker does not only have access to a secret key and to the internal states of the system at the test time. The defender processes an input in multiple channels. Each channel introduces its own randomization in a special transform domain based on a secret key shared between the training and testing stages. Such a transform based randomization with a shared key preserves the gradients in key-defined sub-spaces for the defender but it prevents gradient back propagation and the creation of various bypass systems for the attacker. An additional benefit of multi-channel randomization is the aggregation that fuses soft-outputs from all channels, thus increasing the reliability of the final score. The sharing of a secret key creates an information advantage to the defender. Experimental evaluation demonstrates an increased robustness of the proposed method to a number of known state-of-the-art attacks.

This paper proposes a group membership verification protocol preventing the curious but honest server from reconstructing the enrolled signatures and inferring the identity of querying clients. The protocol quantizes the signatures into discrete embeddings, making reconstruction difficult. It also aggregates multiple embeddings into representative values, impeding identification. Theoretical and experimental results show the trade-off between the security and the error rates.

In recent years, printable graphical codes have attracted a lot of attention enabling a link between the physical and digital worlds, which is of great interest for the IoT and brand protection applications. The security of printable codes in terms of their reproducibility by unauthorized parties or clonability is largely unexplored. In this paper, we try to investigate the clonability of printable graphical codes from a machine learning perspective. The proposed framework is based on a simple system composed of fully connected neural network layers. The results obtained on real codes printed by several printers demonstrate a possibility to accurately estimate digital codes from their printed counterparts in certain cases. This provides a new insight on scenarios, where printable graphical codes can be accurately cloned.

In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA). The STCA is a generalization of randomization techniques which includes random projections, lossy quantization, and addition of ambiguization noise to satisfy the privacy-utility trade-off requirements. The theoretical privacy-preserving properties of STCA have been validated on synthetic data. However, the applicability of STCA to real data and potential threats linked to reconstruction based on recent deep reconstruction algorithms are still open problems. Our results demonstrate that STCA still achieves the claimed theoretical performance when facing deep reconstruction attacks for the synthetic i.i.d. data, while for real images special measures are required to guarantee proper protection of the templates.

In this paper, we address a problem of machine learning system vulnerability to adversarial attacks. We propose and investigate a Key based Diversified Aggregation (KDA) mechanism as a defense strategy. The KDA assumes that the attacker (i) knows the architecture of classifier and the used de-fense strategy, (ii) has an access to the training data set but (iii) does not know the secret key. The robustness of the system is achieved by a specially designed key based randomization. The proposed randomization prevents the gradients' back propagation or the creating of a "bypass" system. The randomization is performed simultaneously in several channels and a multi-channel aggregation stabilizes the results of randomization by aggregating soft outputs from each classifier in multi-channel system. The performed experimental evaluation demonstrates a high robustness and universality of the KDA against the most efficient gradient based attacks like those proposed by N. Carlini and D. Wagner [1] and the non-gradient based sparse adversarial perturbations like OnePixel attacks [2].

In this paper, we propose a privacy preserving framework for outsourced media search applications. Considering three parties, a data owner, clients and a server, the data owner out-sources the description of his data to an external server, which provides a search service to clients on the behalf of the data owner. The proposed framework is based on a sparsifying transform with ambiguization, which consists of a trained linear map, an element-wise nonlinearity and a privacy amplification. The proposed privacy amplification technique makes it infeasible for the server to learn the structure of the database items and queries. We demonstrate that the privacy of the database outsourced to the server as well as the privacy of the client are ensured at a low computational cost, storage and communication burden.

A learning-based framework for representation of domain-specific images is proposed where joint compression and denoising can be done using a VQ-based multi-layer network. While it learns to compress the images from a training set, the compression performance is very well generalized on images from a test set. Moreover, when fed with noisy versions of the test set, since it has priors from clean images, the network also efficiently denoises the test images during the reconstruction. The proposed framework is a regularized version of the Residual Quantization (RQ) where at each stage, the quantization error from the previous stage is further quantized. Instead of codebook learning from the k-means which over-trains for high-dimensional vectors, we show that only generating the codewords from a random, but properly regularized distribution suffices to compress the images globally and without the need to resort to patch-based division of images. The experiments are done on the CroppedYale-B set of facial images and the method is compared with the JPEG-2000 codec for compression and BM3D for denoising, showing promising results.

This paper addresses the problem of Approximate Nearest Neighbor (ANN) search in pattern recognition where feature vectors in a database are encoded as compact codes in order to speed-up the similarity search in large-scale databases. Considering the ANN problem from an information-theoretic perspective, we interpret it as an encoding, which maps the original feature vectors to a less entropic sparse representation while requiring them to be as informative as possible. We then define the coding gain for ANN search using information-theoretic measures. We next show that the classical approach to this problem, which consists of binarization of the projected vectors is sub-optimal. Instead, a properly designed ternary encoding achieves higher coding gains and lower complexity.

In this paper, we compare two methods that can be used by the anti-counterfeiting industry to protect physical objects, which are either based on an object's natural randomness or on artificial randomness embedded on the object. We show that the considered verification architectures rely either on a comparison between an enrolled fingerprint and an extracted one or between a tag and a fingerprint. We compare these setups from detection-theoretic perspectives for both types of architectures. Authentication performance using false and miss error probabilities of the two systems are analysed and then compared using two practical setups. We highlight the advantages and limitations of each architecture. These theoretical results derived for binary fingerprints are useful to construct and optimise practical methods and to help select the appropriate architecture.

This papers presents an analysis on Active Content Fingerprint (aCFP) for local (patch based) image descriptors. A generalization is proposed, the reduction of the aCFP with linear modulation to a constrained projection problem is shown and the optimal solution is given. The constrained projection problem addresses the linear modulation by a constraint on the properties of the resulting local descriptor. A computer simulation using local image patches, extracted from publicly available data sets is provided, demonstrating the advantages under several signal processing distortions.

This paper presents solutions to the local patch based Active Content Fingerprint (aCFP) with linear modulation, general linear feature map and convex constraints on the properties of the local feature descriptor. A direct approximation of the linear feature map such that the image distortion is as small as possible and the approximate linear feature map is as close as possible to the original map is proposed. Then an explicit regularization of the trade-off between the modulation distortion and the robustness of the local feature is introduced trough a novel problem formulation. A computer simulation using local image patches, extracted from publicly available data set is provided, demonstrating the advantages under: additive white Gaussian noise (AWGN), lossy JPEG compression and projective geometrical transform distortions.

Content identification based on digital content fingerprinting attracts significant attention in different emerging applications. In this paper, we consider content identification based on the sign-magnitude decomposition of fingerprint codewords and analyze the achievable rates for sign and magnitude components. We demonstrate that the bit robustness in the sign channel, often used in binary fingerprinting, is determined by the value of the corresponding magnitude component. Correspondingly, one can distinguish between two systems depending how the information about the magnitude component is used at the decoding process, i.e., hard fingerprinting when this information is disregarded, and soft fingerprinting when this information is used. To reveal the advantages of soft information at the decoding, we consider a case of soft fingerprinting where the decoder has access to the complete information about the uncoded magnitude component. However, since it requires a lot of extra memory storage or secure communication, the magnitude information is often quantized to a single bit or extracted directly from the noisy observation. To generalize the existing methods and estimate the impact of quantization and noise in the side information about the magnitude components on the achievable rate, we introduce a channel splitting approach and reveal certain interesting phenomena related to channel polarization. We demonstrate that under proper quantization of the magnitude component, one can clearly observe the existence of strong components whose sign is very robust, even to strong distortions. We demonstrate that under certain conditions, a great portion of the rate in the sign channel is concentrated in strong channel components. Finally, we demonstrate how to use the channel splitting property in the design of efficient low-complexity identification methods.

Many state-of-the-art methods in image retrieval, classification and copy detection are based on the Bag-of-Features (BOF) framework. However, the performance of these systems is mostly experimentally evaluated and little results are reported on theoretical performance. In this paper, we present a statistical framework that makes it possible to analyse the performance of a simple BOF-system and to better understand the impact of different design elements such as the robustness of descriptors, the accuracy of encoding/assignment, information preserving pooling and finally decision making. The proposed framework can be also of interest for a security and privacy analysis of BOF systems.

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.

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.

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.

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

This paper comes to fill a gap in watermarking theory, analyzing the exact performance of the scalar Costa scheme (SCS) facing additive Gaussian attacks when the usual approximation of high-resolution quantization is not valid, thus taking into account the host statistics. The analysis is focused on the assessment of the probability of error, showing new results, an although it is valid in a general scenario, its practical interest is increased when SCS is used in conjunction with the so-called spread-transform. The accomplished reformulation of the problem also permits to show that the achievable rate of SCS is never worse than that of classical spread-spectrum-based methods, as it was thought so far, and allows to establish interesting links with spread spectrum and the Improved Spread Spectrum method.

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.

Text documents, in electronic and hardcopy forms, are and will probably remain the most widely used kind of content in our digital age. The goal of this paper is to overview protocols for text data-hiding based "smart documents", achieving document self-authentication, self-recovery, self-annotation and automatic processing. We argue that document security, recovery and embedded annotation are the most promising data-hiding based frameworks.

By abandoning the assumption of an infinite document to watermark ratio, we recompute the achievable rates for Egger's scalar Costa scheme (SCS, also known as scalar distortion compensated dither modulation) and show, as opposed to the results reported by Eggers, that the achievable rates of SCS are always larger than those of spread spectrum (SS). Moreover, we show that for small watermark to noise ratios, SCS equivalent to a two-centroid problem, thus revealing interesting relations with SS and with Malvar's improved spread spectrum (ISS). We also show an interesting behavior for the optimal distortion compensation parameter. All these results aim at filling an existing gap in watermarking theory and have important consequences for the design of efficient decoders for data hiding problems.

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.

In this paper we consider the problem of joint capacity-security analysis of data hiding technologies from the communications point of view. First, we formulate data hiding as an optimal encoding problem for different operational regimes, that include both robust digital watermarking and steganography. This provides the corresponding estimation of the hidden data statistics, as well as of the rates approaching embedding capacity. Secondly, we formulate the problem of blind stochastic hidden data detection based on the developed watermark statistics. Finally, we estimate the error of watermark detection and the variance of the watermark estimation that determine the system security.

This paper presents an efficient method for the estimation and recovering from nonlinear or local geometrical distortions, such as the random bending attack and restricted projective transforms. The distortions are modeled as a set of local affine transforms, the watermark being repeatedly allocated into small blocks in order to ensure its locality. The estimation of the affine transform parameters is formulated as a robust penalized maximum likelihood (ML) problem, which is suitable for the local level as well as for global distortions. Results with the Stirmark benchmark confirm the high robustness of the proposed method and show its state-of-the-art performance.

We describe effective channel coding strategies which can be used in conjunction with linear programming optimization techniques for the embedding of robust perceptually adaptive DCT domain watermarks. The main contributions lie in the proposal of a coding strategy based on the magnitude of a DCT coefficient, the use of turbo codes for effective error correction, and finally the incorporation of JPEG quantization tables at embedding.