We investigate non-unique artifacts in images captured by Samsung smartphones and their impact on PRNU-based source camera verification. These artifacts, linked to Exynos-equipped devices, manifest as 6 distinct periodic patterns identified across models released between 2018 and 2025, and lead to fingerprint collisions that degrade verification performance. For the most prevalent pattern, we restore reliable verification by applying SVD to separate block-induced artifacts from the underlying PRNU. We further exploit the regularity of these artifacts to estimate and compensate for HDR-induced local misalignments, enabling fingerprint synchronization in HDR images. Experimental results show that combining SVD-based PRNU recovery with HDR-aware synchronization significantly improves detection performance, in several cases approaching perfect verification. While newer artifact patterns remain more challenging, this work constitutes a first step toward mitigating fingerprint collisions in Samsung devices.

iPhone portrait-mode images contain a distinctive pattern in out-of-focus regions simulating the bokeh effect, which we term Apple’sSynthetic Defocus Noise Pattern(SDNP). If overlooked, this pattern can interfere with blind forensic analyses, especially PRNU-based camera source verification, as noted in earlier works. Since Apple’s SDNP remains underexplored, we provide a detailed characterization, proposing a method for its precise estimation, modeling its dependence on scene brightness, ISO settings, and other factors. Leveraging this characterization, we explore forensic applications of the SDNP, including traceability of portrait-mode images across iPhone models and iOS versions in open-set scenarios, assessing its robustness under post-processing. Furthermore, we show that masking SDNP-affected regions in PRNU-based camera source verification significantly reduces false positives, overcoming a critical limitation in camera attribution, and improving state-of-the-art techniques.

Federated Learning (FL) is a promising solution for training machine learning models on data that may contain personal or sensitive information, allowing different data-owners to provide local training updates to a shared model while keeping their data on their own premises. To protect the model from potential misuse or leakage, previous works on FL watermarking have proposed both white-box and black-box schemes, more recently including collusion-resistant traitor tracing capabilities, providing a unique model instance to each data-owner. In case of a leak, the suspected model can be traced back to its origin, even if multiple malicious participants collude. However, there is still a large margin for improving the collusion-resistance capabilities of black-box schemes, where the watermark is embedded into the model’s input-output behavior. This work aims at shedding light on this challenging aspect and demonstrates that collusion-resistance can be improved through the identification by the embedder of the relevant weights across different model instances.

MOTIVATION: The affordability of genome sequencing and the widespread availability of genomic data have opened up new medical possibilities. Nevertheless, they also raise significant concerns regarding privacy due to the sensitive information they encompass. These privacy implications act as barriers to medical research and data availability. Researchers have proposed privacy-preserving techniques to address this, with cryptography-based methods showing the most promise. However, existing cryptography-based designs lack (i) interoperability, (ii) scalability, (iii) a high degree of privacy (i.e. compromise one to have the other), or (iv) multiparty analyses support (as most existing schemes process genomic information of each party individually). Overcoming these limitations is essential to unlocking the full potential of genomic data while ensuring privacy and data utility. Further research and development are needed to advance privacy-preserving techniques in genomics, focusing on achieving interoperability and scalability, preserving data utility, and enabling secure multiparty computation. RESULTS: This study aims to overcome the limitations of current cryptography-based techniques by employing a multi-key homomorphic encryption scheme. By utilizing this scheme, we have developed a comprehensive protocol capable of conducting diverse genomic analyses. Our protocol facilitates interoperability among individual genome processing and enables multiparty tests, analyses of genomic databases, and operations involving multiple databases. Consequently, our approach represents an innovative advancement in secure genomic data processing, offering enhanced protection and privacy measures. AVAILABILITY AND IMPLEMENTATION: All associated code and documentation are available at https://github.com/farahpoor/smkhe.

Forensic image source attribution aims at deciding whether a query image was taken by a specific camera. While various algorithms leveraging forensic traces have been proposed, the most effective techniques rely on Photo Response Non-Uniformity (PRNU), a pattern introduced by camera sensors during the image acquisition process. In recent years, advances in image acquisition and processing technologies in modern devices have been found to impact the performance of PRNU, seemingly challenging its uniqueness. In this paper, we build upon recent discoveries of leaks in PRNU uniqueness, focusing on the dataset recently published by Iuliani et al. which has been instrumental in identifying numerous issues related to source attribution. Specifically, we analyze the effects in terms of false positive of visible watermarks applied to Xiaomi Mi 9 images, and reveal artifacts in the magnitude of the Discrete Fourier Transform of Samsung A50 images, indicative of the absence of non-unique artifacts. Furthermore, we demonstrate how several false positive cases are attributed to mislabeled devices. Finally, we show that a number of false negatives from the dataset are traceable to radially corrected images, and to images processed by third-party software that had not been previously noticed.

Radial distortion correction, applied by in-camera or out-camera software/firmware alters the supporting grid of the image so as to hamper PRNU-based camera attribution. Existing solutions to deal with this problem try to invert/estimate the correction using radial transformations parameterized with few variables in order to restrain the computational load; however, with ever more prevalent complex distortion corrections their performance is unsatisfactory. In this paper we propose an adaptive algorithm that by dividing the image into concentric annuli is able to deal with sophisticated corrections like those applied out-camera by third party software like Adobe Lightroom, Photoshop, Gimp and PT-Lens. We also introduce a statistic called cumulative peak of correlation energy (CPCE) that allows for an efficient early stopping strategy. Experiments on a large dataset of in-camera and out-camera radially corrected images and on a in-the-wild dataset of images from smartphones show that our solution improves the state of the art in terms of both accuracy and computational cost.

More complex and ever more common lens distortion correction post-processing is seriously hampering state-of-the-art camera attribution techniques. In this paper, we show that the two main existing techniques, namely PRNU (Photo Response Non Uniformity)-based and linear-pattern-based, can be successfully combined to improve performance. Moreover, we introduce a novel method that is able to correctly invert adaptive distortion correction transformations by successively maximizing the peak-to-correlation energy (PCE) and the linear-pattern energy for much more reliable camera attribution. A novel validation procedure to quickly discard mismatched test images is also proposed. Finally, we show how great reductions in running time can be achieved by using a GPU for interpolation, resampling, and PCE computation. The code is available at https://github.com/AMontiB/PSLR.

The photoresponse non-uniformity (PRNU) is a camera-specific pattern, which acts as unique fingerprint of any imaging sensor and thus is widely adopted to solve multimedia forensics problems such as device identification or forgery detection. Customarily, the theoretical analysis of this fingerprint relies on a multiplicative model for the denoising residuals. This setup assumes that the nonlinear mapping from scene irradiance to preprocessed luminance, that is, the composition of the Camera Response Function (CRF) with the digital preprocessing pipeline, is a gamma correction. However, this assumption seldom holds in practice. In this paper, we improve the multiplicative model by including the influence of this nonlinear mapping, termed PRNU emphasis, on the denoising residuals. On the theoretical side, we conduct first an exploratory analysis to show that the response of typical cameras deviates from a gamma correction. We also propose a regularized least squares estimator to measure this effect. On the practical side, we argue that the PRNU emphasis is especially beneficial for a source camera attribution problem with cropped images. We back our argument with an extensive empirical evaluation using different denoisers and both compressed and uncompressed images. This new model will pave the way to future PRNU estimators and detectors.

Video stabilization is an in-camera processing commonly applied by modern acquisition devices. While significantly improving the visual quality of the resulting videos, it has been shown that such operation typically hinders the forensic analysis of video signals. In fact, the correct identification of the acquisition source usually based on Photo Response non-Uniformity (PRNU) is subject to the estimation of the transformation applied to each frame in the stabilization phase. A number of techniques have been proposed for dealing with this problem, which however typically suffer from a high computational burden due to the grid search in the space of inversion parameters. Our work attempts to alleviate these short-comings by exploiting the parallelization capabilities of Graphics Processing Units (GPUs), typically used for deep learning applications, in the framework of stabilised frames inversion. Moreover, we propose to exploit SIFT features to estimate the camera momentum and identify less stabilized temporal segments, thus enabling a more accurate identification analysis, and to efficiently initialize the frame-wise parameter search of consecutive frames. Experiments on a consolidated benchmark dataset confirm the effectiveness of the proposed approach in reducing the required computational time and improving the source identification accuracy. The code is available at https://github.com/AMontiB/GPU-PRNU-SIFT.

The demand for methods to protect the Intellectual Property Rights (IPR) associated to Deep Neural Networks (DNNs) is rising. Watermarking has been recently proposed as a way to protect the IPR of DNNs and track their usages. Although a number of techniques for media watermarking have been proposed and developed over the past decades, their direct translation to DNN watermarking faces the problem of the embedding being carried out on functionals instead of signals. This originates differences not only in the way performance, robustness and unobtrusiveness are measured, but also on the embedding domain, since there is the possibility of hiding information in the model behavior. In this paper, we discuss these dissimilarities that lead to a DNN-specific taxonomy of watermarking techniques. Then, we present four challenges specific to DNN watermarking that, for their practical importance and theoretical interest, should occupy the agenda of researchers in the next years. Finally, we discuss some bad practices that negatively affected research in media watermarking and that should not be repeated in the case of DNNs.

Abstract Camera fingerprints based on sensor PhotoResponse Non-Uniformity (PRNU) have gained broad popularity in forensic applications due to their ability to univocally identify the camera that captured a certain image. The fingerprint of a given sensor is extracted through some estimation method that requires a few images known to be taken with such sensor. In this paper, we show that the fingerprints extracted in this way leak a considerable amount of information from those images used in the estimation, thus constituting a potential threat to privacy. We propose to quantify the leakage via two measures: one based on the Mutual Information, and another based on the output of a membership inference test. Experiments with practical fingerprint estimators on a real-world image dataset confirm the validity of our measures and highlight the seriousness of the leakage and the importance of implementing techniques to mitigate it. Some of these techniques are presented and briefly discussed.

Non-static scenes represent one of the main barriers for retrieving the electrical network frequency (ENF) from digital videos recorded in realistic scenarios, since movement influences the luminance of pixels, hindering the recovery of the desired information. Aiming to mitigate the effects of changes in the scene, in this letter a video processing stage, that detects and combines those pixels that are not affected by movement, is proposed. The sequence obtained thereby is delivered to a PLL-based FM demodulator, that estimates the ENF taking into account its autoregressive nature. Additionally, a frame rate estimation algorithm is employed to avoid time-consuming trial and error loops in the extraction process. The performance of the system is tested by computing the probability of estimating correctly the time of recording of a given video, using ground-truth sequences obtained directly from the mains or a reliable database.

The Variation of Prediction Footprint (VPF), formerly used in video forensics for double compression detection and GOP size estimation, is comprehensively investigated to improve its acquisition capabilities and extend its use to video sequences that contain bi-directional frames (B-frames). By relying on a universal rate-distortion analysis applied to a generic double compression scheme, we first explain the rationale behind the presence of the VPF in double compressed videos and then justify the need of exploiting a new source of information such as the motion vectors, to enhance the VPF acquisition process. Finally, we describe the shifted VPF induced by the presence of B-frames and detail how to compensate the shift to avoid misguided GOP size estimations. The experimental results show that the proposed Generalized VPF (G-VPF) technique outperforms the state of the art, not only in terms of double compression detection and GOP size estimation, but also in reducing computational time.

Location Privacy-Preserving Mechanisms (LPPMs) in the literature largely consider that users' data available for training wholly characterizes their mobility patterns. Thus, they hardwire this information in their designs and evaluate their privacy properties with these same data. In this paper, we aim to understand the impact of this decision on the level of privacy these LPPMs may offer in real life when the users' mobility data may be different from the data used in the design phase. Our results show that, in many cases, training data does not capture users' behavior accurately and, thus, the level of privacy provided by the LPPM is often overestimated. To address this gap between theory and practice, we propose to use blank-slate models for LPPM design. Contrary to the hardwired approach, that assumes known users' behavior, blank-slate models learn the users' behavior from the queries to the service provider. We leverage this blank-slate approach to develop a new family of LPPMs, that we call Profile Estimation-Based LPPMs. Using real data, we empirically show that our proposal outperforms optimal state-of-the-art mechanisms designed on sporadic hardwired models. On non-sporadic location privacy scenarios, our method is only better if the usage of the location privacy service is not continuous. It is our hope that eliminating the need to bootstrap the mechanisms with training data and ensuring that the mechanisms are lightweight and easy to compute help fostering the integration of location privacy protections in deployed systems.

Multimedia contents are inherently sensitive signals that must be protected when processed in untrusted environments. The field of Secure Signal Processing addresses this challenge by developing methods which enable operating with sensitive signals in a privacy-conscious way. Recently, we introduced a hard lattice problem called m-RLWE (multivariate Ring Learning with Errors) which gives support to efficient encrypted processing of multidimensional signals. Afterwards, Bootland et al. presented an attack to m-RLWE that reduces the security of the underlying scheme from a lattice with dimension \prod_in_i to \max\n_i\ _i . Our work introduces a new pre-/post-coding block that addresses this attack and achieves the efficient results of our initial approach while basing its security directly on RLWE with dimension \prod_in_i, hence preserving the security and efficiency originally claimed. Additionally, this work provides a detailed comparison between a conventional use of RLWE, m-RLWE and our new pre-/post-coding procedure, which we denote "packed''-RLWE. Finally, we discuss a set of encrypted signal processing applications which clearly benefit from the proposed framework, either alone or in a combination of baseline RLWE, m-RLWE and "packed''-RLWE.

In the last decade, the extremely rapid proliferation of digital devices capable of acquiring and sharing images over the Web has significantly increased the amount of digital images publicly accessible by everyone with Internet access. Despite the obvious benefits of such technological improvements, it is becoming mandatory to verify the origin and trustfulness of such shared pictures. Photo response non-uniformity (PRNU) is the reference signal for forensic investigators when it comes to verifying or identifying which camera device shot a picture under analysis. In spite of this, PRNU is almost a white-shaped noise, thus being very difficult to compress for storage or large scale search purposes, which are frequent investigation scenarios. To overcome the issue, the forensic community has developed a series of compression algorithms. Lately, Gaussian random projections have proved to achieve state-of-the-art performance. In this paper, we propose two additional steps that help improving even more Gaussian random projections compression rate: 1) a decimation preprocessing step tailored at attenuating frequency components in which PRNU traces are already suppressed in JPEG compressed images and 2) a dead-zone quantizer (rather than the commonly used binary one) that enables an entropy coding scheme to save bitrate when storing PRNU fingerprints or sending residuals over a communication channel. Reported results show the effectiveness of proposed improvements, both under controlled JPEG compression and in a real case scenario.

In the last years we have witnessed the appearance of a variety of strategies to design optimal location privacy-preserving mechanisms, in terms of maximizing the adversary's expected error with respect to the users' whereabouts. In this work, we take a closer look at the defenses created by these strategies and show that, even though they are indeed optimal in terms of adversary's correctness, not all of them offer the same protection when looking at other dimensions of privacy. To avoid "bad" choices, we argue that the search for optimal mechanisms must be guided by complementary criteria. We provide two example auxiliary metrics that help in this regard: the conditional entropy, that captures an information-theoretic aspect of the problem; and the worst-case quality loss, that ensures that the output of the mechanism always provides a minimum utility to the users. We describe a new mechanism that maximizes the conditional entropy and is optimal in terms of average adversary error, and compare its performance with previously proposed optimal mechanisms using two real datasets. Our empirical results confirm that no mechanism fares well on every privacy criteria simultaneously, making apparent the need for considering multiple privacy dimensions to have a good understanding of the privacy protection a mechanism provides.

In this work, we address the problem of designing delay-based anonymous communication systems. We consider a timed mix where an eavesdropper wants to learn the communication pattern of the users, and study how the mix must delay the messages so as to increase the adversary's estimation error. We show the connection between this problem and a MIMO system where we want to design the coloring filter that worsens the adversary's estimation of the MIMO channel matrix. We obtain theoretical solutions for the optimal filter against short-term and long-term adversaries, evaluate them with experiments, and show how some properties of filters can be used in the implementation of timed mixes. This opens the door to the application of previously known filter design techniques to anonymous communication systems.

Recent advances in Next Generation Sequencing have increased the availability of genomic data for more accurate analyses, like testing for the genetic susceptibility to a disease. Current laboratories' facilities cannot cope with this data growth, and genomic processing needs to be outsourced, comprising serious privacy risks. This work proposes an encrypted genomic susceptibility test protocol based on lattice homomorphic cryptosystems, and introduces optimizations like data packing and transformed processing to achieve considerable gains in performance, bandwidth and storage needs.

Intrinsic statistical properties of natural uncompressed images are used in image forensics for detecting the traces of previous processing operations. In this paper, we propose novel forensic detectors of JPEG compression traces in images stored in uncompressed formats, based on a theoretical analysis of Benford-Fourier coefficients computed on the 8 × 8 block-Discrete Cosine Transform (DCT) domain. In fact, the distribution of such coefficients is derived theoretically both under the hypotheses of no compression and previous compression with a certain quality factor, allowing for the computation of the respective likelihood functions. Then, two classification tests based on different statistics are proposed, both relying on a discriminative threshold that can be determined without the need of any training phase. The statistical analysis is based on the only assumptions of generalized Gaussian distribution of DCT coefficients and independence among DCT frequencies, thus resulting in robust detectors applying to any uncompressed image. In fact, experiments on different datasets show that the proposed models are suitable for the images of different sizes and source cameras, thus overcoming dataset-dependence issues that typically affect the state-of-art techniques.

Multimedia contents are inherently sensitive signals that must be protected whenever they are outsourced to an untrusted environment. This problem becomes a challenge when the untrusted environment must perform some processing on the sensitive signals; a paradigmatic example is Cloud-based signal processing services. Approaches based on Secure Signal Processing (SSP) address this challenge by proposing novel mechanisms for signal processing in the encrypted domain and interactive secure protocols to achieve the goal of protecting signals without disclosing the sensitive information they convey. This paper presents a novel and comprehensive set of approaches and primitives to efficiently process signals in an encrypted form, by using Number Theoretic Transforms (NTTs) in innovative ways. This usage of NTTs paired with appropriate signal pre-and post-coding enables a whole range of easily composable signal processing operations comprising, among others, filtering, generalized convolutions, matrix-based processing or error correcting codes. Our main focus is on unattended processing, in which no interaction from the client is needed; for implementation purposes, efficient lattice-based somewhat homomorphic cryptosystems are used. We exemplify these approaches and evaluate their performance and accuracy, proving that the proposed framework opens up a wide variety of new applications for secured outsourced-processing of multimedia contents.

The forensic analysis of resampling traces in upscaled images is addressed via subspace decomposition and random matrix theory principles. In this context, we derive the asymptotic eigenvalue distribution of sample autocorrelation matrices corresponding to genuine and upscaled images. To achieve this, we model genuine images as an autoregressive random field and we characterize upscaled images as a noisy version of a lower dimensional signal. Following the intuition behind Marčenko-Pastur law, we show that for upscaled images, the gap between the eigenvalues corresponding to the low-dimensional signal and the ones from the background noise can be enhanced by extracting a small number of consecutive columns/rows from the matrix of observations. In addition, using bounds provided by the same law for the eigenvalues of the noise space, we propose a detector for exposing traces of resampling. Finally, since an interval of plausible resampling factors can be inferred from the position of the gap, we empirically demonstrate that by using the resulting range as the search space of existing estimators (based on different principles), a better estimation accuracy can be attained with respect to the standalone versions of the latter.

Two sequential camera fingerprint detection methods are proposed. Sequential tests implement a log-likelihood ratio test in an incremental way, thus enabling a reliable decision with a minimal number of observations. One of our methods adapts Goljan et al.'s to sequential operation. The second, which offers better performance in terms of average number of test observations, is based on treating the alternative hypothesis as a doubly stochastic model. Finally, we validate the performance of our methods with experiments and compare them with the state of the art in fast camera fingerprint detection.

The field of genomic research has considerably grown in the recent years due to the unprecedented advances brought about by Next Generation Sequencing (NGS) and the need and increasing widespread use of outsourced processing. But this rapid increase also poses severe privacy risks due to the inherently sensitive nature of genomic information. In this work, we address privacy-preserving genetic susceptibility tests outsourced to an untrustworthy party, enhancing previous approaches in terms of computation and communication efficiency by leveraging the use of somewhat homomorphic lattice encryption and relinearization operations to achieve more efficient constructions. Additionally, we also propose a more general construction which deals with several different medical units (such as pharmaceutical companies or hospitals), managing patients' consent to the disclosure of test results for each of these units, which may dynamically join the system. Our scheme features an attribute-based homomorphic cryptosystem which enables enforcing the patient's access policy referred to the different medical units.

We address the known problem of detecting a previous compression in JPEG images, focusing on the challenging case of high and very high quality factors (>= 90) as well as repeated compression with identical or nearly identical quality factors. We first revisit the approaches based on Benford--Fourier analysis in the DCT domain and block convergence analysis in the spatial domain. Both were originally conceived for specific scenarios. Leveraging decision tree theory, we design a combined approach complementing the discriminatory capabilities. We obtain a set of novel detectors targeted to high quality grayscale JPEG images.

The swift growth of cellular mobile networks in recent years has made voice channels almost accessible everywhere. Besides, data hiding has recently attracted significant attention due to its ability to imperceptibly embed side information that can be used for signal enhancement, security improvement, and two-way authentication purposes. In this regard, we aim at proposing efficient schemes for hiding data in the widespread voice channel of cellular networks. To this aim, our first contribution is to model the channel accurately by considering a linear filter plus a nonlinear scaling function. This model is validated through experiments with true speech signals. Then we leverage on this model to propose two additive and multiplicative data hiding methods based on the spread spectrum techniques. In addition, inspired by the concept of M-ary biorthogonal codes, we develop novel schemes that significantly outperform the previous ones. The performance of all the methods that we present is assessed mathematically and cross-validated with simulations. These are later extended to true speech signals where the results evidence an excellent performance as predicted by the theory. Finally, we assess the imperceptibility by means of both subjective and objective benchmarks and show that the perceptual impact of our watermarks is acceptable.

Current implementations of high-latency anonymous communication systems are based on pool mixes. These tools act as routers that apply a random delay to the messages traversing them, making it hard for an eavesdropper to guess the correspondences between incoming and outgoing messages. This hides the identities of communicating partners in the network, but it does not prevent an adversary continuously monitoring the network from unveiling the communication profiles of the users. In this paper, we tackle the problem of designing the delay characteristic of pool mixes so as to maximize the protection of the users against profiling attacks. First, we propose a theoretical model for users' sending behavior which we validate using three real data sets of a different nature. Then, we use this model to perform a privacy analysis of the system and obtain the delay function of the mix, which is optimal in the sense of protecting the users. Since computing the delay characteristic of this optimal pool mix requires information about the users' behavior, we also propose a user-independent but less effective mix design. We evaluate these pool mixes, comparing them with one of the most studied existing designs, the binomial pool mix. Our experiments show that an adversary against our optimal design may need up to 30 times as long to achieve the same level of disclosure as for a binomial pool mix.

Images are inherently sensitive signals that require privacy-preserving solutions when processed in an untrusted environment, but their efficient encrypted processing is particularly challenging due to their structure and size. This work introduces a new cryptographic hard problem called m-RLWE (multivariate Ring Learning with Errors) extending RLWE. It gives support to lattice cryptosystems that allow for encrypted processing of multidimensional signals. We show an example cryptosystem and prove that it outperforms its RLWE counterpart in terms of security against basis-reduction attacks, efficiency and cipher expansion for encrypted image processing.

Powerful image editing software is nowadays capable of creating sophisticated and visually compelling fake photographs, thus posing serious issues to the trustworthiness of digital contents as a true representation of reality. Digital image forensics has emerged to help regain some trust in digital images by providing valuable aids in learning the history of an image. Unfortunately, in real scenarios, its application is limited, since multiple processing operators are likely to be applied, which alters the characteristic footprints exploited by current forensic tools. In this paper, we develop a novel forensic technique that is able to detect chains of operators applied to an image. In particular, we study the combination of Joint Photographic Experts Group compression and full-frame linear filtering, and derive an accurate mathematical framework to fully characterize the probabilistic distributions of the discrete cosine transform (DCT) coefficients of the quantized and filtered image. We then exploit such knowledge to define a set of features from the DCT distribution and build an effective classifier able to jointly disclose the quality factor of the applied compression and the filter kernel. Extensive experimental analysis illustrates the efficiency and versatility of the proposed approach, which effectively overcomes the state-of-the-art.

A novel complex flat fading channel estimation scheme is proposed. Contrarily to previous schemes in the literature, this new approach is not based on introducing pilot sequences, but on reducing the interference caused by the information-bearing signal on the estimation-aiding signal by using Dirty Paper Coding. We show through simulations that our method outperforms the Partially-Data Dependent scheme, which is a state-of-the-art technique based on superimposed pilots.

First-order statistics of First Significant Digits (FSD) have been recently exploited in multimedia forensics as a powerful tool to reveal traces of previous coding operations. As an answer, adversarial approaches aimed at modifying the FSD histogram and fooling such forensic methods have been proposed. However, the existing techniques have limitations in terms of distortion introduced in the multimedia object. In this paper, a transportation-theoretic formulation of the problem is presented which provides a close-to-optimal solution. Such strategy is tested in a well-known image forensic scenario, where FSDs of 8 × 8-DCT coefficients after single or double quantization are modified in order to restore a certain target histogram and the distortion with respect to the provided compressed image is measured in terms of MSE.

Watermarking schemes based on the Dirty Paper Coding (DPC) paradigm have been shown to achieve much higher rates than classical Spread-Spectrum methods. However, in practice, the latter continue to be used due to their higher security and robustness. In fact, the most prevalent DPC method, the so-called Scalar Costa Scheme (SCS), is prone to non-additive attacks, such as a simple gain which produces a desynchronization between the embedding and decoding codebooks thus severely affecting performance. Although some gain-robust modifications to the basic SCS exist, all have serious drawbacks. One alternative, which was somehow abandoned for its complexity, is to estimate the gain at the decoder, with the advantage of preserving the simplicity of SCS. In this paper we take a new look at the estimation problem and propose an affordable algorithm to perform Maximum Likelihood estimation of the channel gain, that is able to restore the original SCS performance. We also show and experimentally illustrate how our scheme can be effectively adapted to watermark decoding in filtered images.

Intrinsic statistical properties of natural uncompressed images can be used in image forensics for detecting traces of previous processing operations. In this paper, we extend the recent theoretical analysis of Benford-Fourier coefficients and propose a novel forensic detector of JPEG compression traces in images stored in an uncompressed format. The classification is based on a binary hypothesis test for which we can derive theoretically the confidence intervals, thus avoiding any training phase. Experiments on real images and comparisons with state-of-art techniques show that the proposed detector outperforms existing ones and overcomes issues due to dataset-dependency.

Mixes, relaying routers that hide the relation between incoming and outgoing messages, are the main building block of high-latency anonymous communication networks. A number of so-called disclosure attacks have been proposed to effectively deanonymize traffic sent through these channels. Yet, the dependence of their success on the system parameters is not well-understood. We propose the least squares disclosure attack (LSDA), in which user profiles are estimated by solving a least squares problem. We show that LSDA is not only suitable for the analysis of threshold mixes, but can be easily extended to attack pool mixes. Furthermore, contrary to previous heuristic-based attacks, our approach allows us to analytically derive expressions that characterize the profiling error of LSDA with respect to the system parameters. We empirically demonstrate that LSDA recovers users' profiles with greater accuracy than its statistical predecessors and verify that our analysis closely predicts actual performance.

There has been a recent interest in counterforensics as an adversarial approach to forensic detectors. Most of the existing counterforensics strategies, although successful, are based on heuristic criteria, and their optimality is not proven. In this paper the optimal modification strategy of a content in order to fool a histogram-based forensics detector is derived. The proposed attack relies on the assumption of a convex cost function; special attention is paid to the Euclidean norm, obtaining the optimal attack in the MSE sense. In order to prove the usefulness of the proposed strategy, we employ it to successfully attack a well-known algorithm for detecting double JPEG compression.

This paper is a first attempt to provide a unified framework for studying signal processing problems where designers have to cope with the presence of an adversary, including media forensics, watermarking, adversarial machine learning, biometric spoofing, etc. We focus on the binary decision problem and discuss which strategies the adversary can use to flip the decision output at minimal cost, including blind sensitivity attacks and hill-climbing attacks. As the defender can also play smarter by considering the presence of a rational adversary, we introduce a game-theoretic approach where some advances have been recently made. We conclude by discussing some trends raised by this game-theoretic formulation.

The most widely used information lossy source coding schemes for multimedia signals rely on the quantization of the content samples in a linearly transformed domain. A number of forensic applications (e.g., processing history estimation, tampering detection, software identification) can be posed as the estimation of the equivalent lattice quantizer from the observed samples. We present a new lattice estimation algorithm based on the observation of noisy points of the lattice. Although inspired by the work of Neelamani et al., our scheme uses the so-called “dual lattice” to achieve significant performance improvements with respect to its predecessors as measured by the number of vectors of the lattice basis that can be correctly estimated. Such performance improvement is even more dramatic when small pieces of the contents are considered, which indeed is especially relevant for forensic applications.

The characteristic artifacts left in an image by JPEG compression are often exploited to gather information about the processing history of the content. However, linear image filtering, often applied as postprocessing to the entire image (full-frame) for enhancement, may alter these forensically significant features, thus complicating the application of the related forensics techniques. In this paper, we study the combination of JPEG compression and full-frame linear filtering, analyzing their impact on the Discrete Cosine Transform (DCT) statistical properties of the image. We derive an accurate mathematical framework that allows to fully characterize the probabilistic distributions of the DCT coefficients of the quantized and filtered image. We then exploit this knowledge to estimate the applied filter. Experimental results show the effectiveness of the proposed method.

In a wide range of practical multimedia scenarios several correlated contents are available. The aim of this work is to quantify the gain that can be achieved in forensic applications by jointly considering those contents, instead of analyzing them separately. The used tool is the Kullback-Leibler Divergence between the distributions corresponding to different operators; the Maximum Likelihood estimator of the applied operator is also obtained, in order to illustrate how the correlation is exploited for estimation. Our detailed analysis is constrained to the Gaussian case (both for the input signal distribution and the processing randomness) and linear operators. Several practical scenarios are studied, and the relationships between the derived results are established. Finally, the links with Distributed Source Coding are highlighted.

Face recognition is one of the foremost applications in computer vision, which often involves sensitive signals; privacy concerns have been raised lately and tackled by several recent privacy-preserving face recognition approaches. Those systems either take advantage of information derived from the database templates or require several interaction rounds between client and server, so they cannot address outsourced scenarios. We present a private face verification system that can be executed in the server without interaction, working with encrypted feature vectors for both the templates and the probe face. We achieve this by combining two significant contributions: 1) a novel feature model for Gabor coefficients' magnitude driving a Lloyd-Max quantizer, used for reducing plaintext cardinality with no impact on performance; 2) an extension of a quasi-fully homomorphic encryption able to compute, without interaction, the soft scores of an SVM operating on quantized and encrypted parameters, features and templates. We evaluate the private verification system in terms of time and communication complexity, and in verification accuracy in widely known face databases (XM2VTS, FERET, and LFW). These contributions open the door to completely private and noninteractive outsourcing of face verification.

Face recognition is a prominent application of image processing. It is also a very sensitive application, and privacy concerns have been lately raised and tackled in several recent papers dealing with privacy-preserving face recognition systems. Nevertheless, the presented systems either use the knowledge of some information derived from the database templates in order to perform the recognition or require several interaction rounds between client and server. In this paper, we propose a private system that can cope with a simple verification algorithm executed in the server without interaction (using a quasi-fully homomorphic encryption and an efficient face features representation with Lloyd-Max quantized Gabor jets), in which both the templates and the queried face are encrypted; we show its performance in terms of time complexity and size of transferred encryptions, as well as in verification accuracy with respect to the non-private system. This opens the door to completely private and noninteractive outsourcing of face recognition.

Although full-duplex relaying schemes are appealing in order to improve spectral efficiency, simultaneous reception and transmission in the same frequency results in self-interference, distorting the retransmitted signal and making the relay prone to oscillation. Current feedback cancellation techniques by means of adaptive filters are hampered by the fact that the useful and interference signals are highly correlated. We present a new adaptive algorithm which effectively and blindly restores the spectral shape of the desired signal. In contrast with previous schemes, the novel adaptive feedback canceller has low complexity, does not introduce additional delay in the relay station, and partly compensates for multipath propagation.

The insertion of a secondary transmitter in a multicarrier broadcast single frequency network is studied. The secondary information is overlaid on top of the primary waveform, which is also reinforced by the secondary transmitter. The degradation of the primary service due to the presence of echoes in a strong line of sight environment is taken into account, and mitigated with an appropriate filtering at the secondary transmitter. The transmit rate of the secondary system is maximized while keeping the original primary coverage area, defined as a function of a BER bound. The analytical results are verified by means of software simulations and hardware tests, where the importance of the proposed filtering is clearly shown.

In this work Weber's law is followed for designing a perceptually shaped side-informed data hiding scheme. The resulting method is a generalized version of a logarithmic quantization algorithm previously proposed by the authors. Closed formulas for analyzing the embedding power and decoding error probability of this new method are provided, and experimental results showing its good behavior against severe attacks are reported.

The field of Signal Processing in the Encrypted Domain (SPED) has emerged in order to provide efficient and secure solutions for pre serving privacy of signals that are processed by untrusted agents. In this work, we study the privacy problem of adaptive filtering, one of the most important and ubiquitous blocks in signal processing nowadays. We examine several use cases along with their privacy characteristics, constraints and requirements, that differ in several aspects from those of the already tackled linear filtering and classification problems. Due to the impossibility of using a strategy based solely on current homomorphic encryption systems, we pro pose novel secure protocols for a privacy-preserving execution of the BLMS (Block Least Mean Squares) algorithm, combining different SPED techniques, and paying special attention to the trade-off between computational complexity, bandwidth, and the error produced due to finite-precision implementations.

A novel approach is here presented to embed a low-priority data stream into a high-priority data stream for OFDM wireless communications. The main improvement with respect to conventional hierarchical modulations is that the low-priority data stream can be decoded from the received subcarrier symbols without any previous equalization in case of multipath fading channel. As a consequence, the expected performance is independent of the channel estimation method used to equalize the channel. The low-priority signal is superimposed on the high-priority symbols using a dirty paper coding technique. As a result of the analysis of the proposed method, several improvements to reduce the interference on the high-priority symbols are introduced. The performance of our scheme has been compared with that of a conventional hierarchical modulation using DVB-T system parameters, showing a considerable improvement for multipath fading channels.

A novel approach is here presented to superimpose a low-priority data stream on a high-priority data stream for OFDM systems. The main improvement with respect to conventional hierarchical modulation, which is included with the same purpose in various wireless technologies, is that the low-priority data stream can be decoded from the received subcarrier symbols without any previous equalization for multipath fading channels. As a consequence, the expected performance is nearly invariant of the channel estimation method used to equalize the channel. The low-priority stream is inserted adopting a gain-invariant dirty paper coding based on Rational Dither Modulation, which was proposed for data hiding applications. In this paper an analysis of the developed system is presented and several simulations have been carried out using DVB-T system parameters to verify the validity of the proposed approach. The experimental results show the better performance of the proposed method with respect to that of a conventional hierarchical modulation, particularly when the accuracy of the estimated channel response decreases.

In an increasingly connected world, the protection of digital data when it is processed by other parties has arisen as a major concern for the general public, and an important topic of research. The field of Signal Processing in the Encrypted Domain (SPED) has emerged in order to provide efficient and secure solutions for preserving privacy of signals that are processed by untrusted agents. In this work, we study the privacy problem of adaptive filtering, one of the most important and ubiquitous blocks in signal processing today. We present several use cases for adaptive signal processing, studying their privacy characteristics, constraints, and requirements, that differ in several aspects from those of the already tackled linear filtering and classification problems. We show the impossibility of using a strategy based solely on current homomorphic encryption systems, and we propose several novel secure protocols for a privacy-preserving execution of the least mean squares (LMS) algorithm, combining different SPED techniques, and paying special attention to the error analysis of the finite-precision implementations. We seek the best trade-offs in terms of error, computational complexity, and used bandwidth, showing a comparison among the different alternatives in these terms, and we provide the experimental results of a prototype implementation of the presented protocols, as a proof of concept that showcases the viability and efficiency of our novel solutions. The obtained results and the proposed solutions are straightforwardly extensible to other adaptive filtering algorithms, providing a basis and master guidelines for their privacy-preserving implementation.

Gabor filters have demonstrated their effectiveness in automatic face recognition, which can greatly benefit from an accurate statistical model for Gabor-based face representations. Previous approaches have modeled real and imaginary parts independently as Generalized Gaussians (GG). Since most Gabor-based face recognition systems discard coefficients' phase, we propose a novel statistical model for the magnitude of Gabor coefficients that accounts for the dependence between real and imaginary parts, assuming they are circularly symmetric and marginally GG distributed. The quality of the fit for our model is assessed using the Kullback-Leibler divergence, and optimal quantization of Gabor coefficients is shown as one of its applications.

Detection of resampling traces for digital image blind authentication has been addressed recently by A. C. Gallagher and later extended by B. Mahdian and S. Saic. On the other side, it is well known from the synchronization area in communications that prefiltering is an appropriate tool to improve the performance of those schemes exploiting the underlying cyclostationarity of communication signals. Thus, the detection of resampling manipulations improves significantly when the derivative of the interpolated signal is used for covariance computation. This work focuses on the role of prefiltering as a way of boosting resampling traces and, in particular, on the use of derivation.

In this work, we study the presence of almost cyclostationary fields in images for the detection and estimation of digital forgeries. The almost periodically correlated fields in the two-dimensional space are introduced by the necessary interpolation operation associated with the applied spatial transformation. In this theoretical context, we extend a statistical time-domain test for presence of cyclostationarity to the two-dimensional space. The proposed method allows us to estimate the scaling factor and the rotation angle of resized and rotated images, respectively. Examples of the output of our method are shown and comparative results are presented to evaluate the performance of the two-dimensional extension.

Privacy and security have always been key concerns for individuals. They have also been closely related concepts: in order to increase their perception of security, people sacrifice a part of their privacy by accepting to be surveilled by others. The tradeoff between both is usually reasonable and commonly accepted; however, the case of videosurveillance systems has been particularly controversial since their inception, as their benefits are not perceived to compensate for the privacy loss in many cases. The situation has become even worse during the last years with the massive deployment of these systems, which often do not provide satisfactory guarantees for the citizens. This paper proposes a DRM-based framework for videosurveillance to achieve a better balance between both concepts: it protects privacy of the surveilled individuals, whilst giving support to efficient automated surveillance.

A performance analysis of the authentication method proposed by J. Fridrich and M. Goljan is carried out. This method has the particular feature that both the embedder and the detector generate the watermark from a perceptual digest of the image. Hence, in order to accurately analyze the performance, the digest errors caused by the watermark embedding, the addition of a complementary signal and the scaling attacks are also taken into account.

Generalized Gaussian (GG) densities have been recently proposed to model both real and imaginary parts of Gabor coefficients. However, when matching faces, most systems make use of magnitude information only, due to its smooth behavior with displacements. The first goal of this paper is to propose a novel statistical model for the magnitude of Gabor coefficients, supposed that both real and imaginary parts are GG distributed. The proposed model, namely the ß-Rayleigh distribution, Rß(¿), is a generalization of the standard Rayleigh, R(¿), density. The Kullback Leibler (KL) divergence is used to measure the fitting accuracy of the model, showing the benefits of Rß(¿) over standard R(¿). The second goal of the paper tackles the selection of distance measures for Gabor features comparison, a topic that has received little attention in the literature. Inspired by the proposed statistical model, different ¿ßnorms are tested on the XM2VTS database, showing interesting results that confirm that classical distances used in Gabor-based recognition systems do not provide the best performance.

This work presents a log-stable model for natural images block-variance. Exponential and halfnormal distributions have been previously used to model block-variance, but they were employed to fit images for which the assumption of constant intra-block variance does not hold. We show that when this assumption holds, the log-stable model yields a much better fit in an ML sense. We use a computationally efficient method for estimating the log-stable parameters through the empirical Kullback-Leibler divergence, which is asymptotically optimum in an ML sense, and show the validity of the lognormal distribution as an approximation with closed-form formulas for the ML parameter estimation.

This paper analyzes the performance of the image authentication method based on robust hashing proposed by J. Fridrich and M. Goljan . In this method, both the embedder and the detector generate the watermark from a perceptual digest of the image. Therefore, an accurate performance analysis requires assessing the relation between noise and hash bit errors. Our approach first derives the probability of hash bit error due to watermark embedding and/or the attack, and then uses such probability to derive the probabilities of false positive and false negative.

This paper presents both theoretical and practical analyses of the security offered by watermarking and data hiding methods based on spread spectrum. In this context, security is understood as the difficulty of estimating the secret parameters of the embedding function based on the observation of watermarked signals. On the theoretical side, the security is quantified from an information-theoretic point of view by means of the equivocation about the secret parameters. The main results reveal fundamental limits and bounds on security and provide insight into other properties, such as the impact of the embedding parameters, and the tradeoff between robustness and security. On the practical side, workable estimators of the secret parameters are proposed and theoretically analyzed for a variety of scenarios, providing a comparison with previous approaches, and showing that the security of many schemes used in practice can be fairly low.

This paper presents a security analysis for data-hiding methods based on nested lattice codes, extending the analysis provided by previous works to a more general scenario. The security is quantified as the difficulty of estimating the secret key used in the embedding process, assuming that the attacker has several signals watermarked available with the same secret key. The theoretical analysis accomplished in the first part of this paper quantifies security in an information-theoretic sense by means of the mutual information between the watermarked signals and the secret key, addressing important issues, such as the possibility of achieving perfect secrecy and the impact of the embedding rate in the security level. In the second part, a practical algorithm for estimating the secret key is proposed, and the information extracted is used for implementing a reversibility attack on real images.

Quantization-based methods, such as dither modulation (DM), have gained wide acceptance due to their host rejection capabilities which afford significant performance gains over spread-spectrum-based methods in additive white Gaussian channels. Unfortunately, existing quantization-based schemes are not robust against simple linear-time-invariant (LTI) filtering, which is a common operation with multimedia signals. We propose a new algorithm, named discrete Fourier transform-rational dither modulation (DFT-RDM) which is robust against LTI filtering and yet does not assume any prior knowledge of the filter at either the embedder or the detector. DFT-RDM basically combines a DFT operation with a quantization-based scheme robust to amplitude scaling. Two easily implementable improvements over the basic DFT-RDM are proposed: windowing and spreading. In particular, the latter leads to performance gains that are much larger than those achieved with spreading in regular DM. We also provide a thorough analysis of our scheme which leads to both accurate predictions and bounds on the per-DFT-channel bit-error rate, for the basic DFT-RDM and its combination with spreading and windowing. These tools let the designer choose the main embedding parameters without actually requiring any simulation. The results of several simulations for practical filters validating our analysis are presented as well. The benefits of combining DFT-RDM with windowing, spreading, and Reed-Solomon channel coding are illustrated with an example.

In this paper the authors analyze how the description and presentation of results about an algorithm proposed in the literature should be modified in order to comply with the reproducible signal processing paradigm. We describe the problems one is faced with, by specifically focusing on how the description of the algorithm should be improved with respect to the classical approach.

We introduce a novel asymmetric watermarking scheme, involving a private key for embedding and a public key for detection, and we detail its statistical analysis, relying on Neyman-Pearson criterion. The proposed scheme solves part of the problems connected to previous watermarking approaches based on linear algebra. In particular, special attention is paid at reducing the side information required at the detector, as well as at achieving higher robustness by enphasizing the contribution of the watermark in the detection phase.

Scaling attacks are well-known to be some of the most harmful strategies against quantization-based watermarking methods, as they can completely ruin the performance of the watermarking system with almost no perceptual impact on the watermarked signal. In this paper we propose a new family of quantization-based methods specifically devised to deal with those attacks, and which presents the desirable property of yielding perceptually shaped watermarks.

Gabor filters are biologically motivated convolution kernels that have been widely used in the field of computer vision and, specially, in face recognition during the last decade. This paper proposes a statistical model of Gabor coefficients extracted from face images using generalized Gaussian distributions (GGD's). By measuring the Kullback-Leibler distance (KLD) between the pdf of the GGD and the relative frequency of the coefficients, we conclude that GGD's provide an accurate modeling. The underlying statistics allow us to reduce the required amount of data to be stored (i.e. data compression) via Lloyd-Max quantization. Verification experiments on the XM2VTS database show that performance does not drop when, instead of the original data, we use quantized coefficients.

We present a generalization of Benford’s law for the first significant digit. This generalization is based on keeping two terms of the Fourier expansion of the probability density function of the data in the modular logarithmic domain. We prove that images in the Discrete Cosine Transform domain closely follow this generalization. We use this property to propose an application in image steganalysis, namely, detecting that a given image carries a hidden message.

Scalar spread transform dither modulation (SSTDM), together with its variants, is one of the foremost side-informed data hiding methods. Because of the projection stage of additive spread spectrum (Add-SS), SSTDM achieves the same spreading gain as the former, so that both become robust to additive attacks. We show that the similarities between Add-SS and SSTDM do not extend to the popular cropping attack, since when the removed area increases, performance degrades smoothly for the former but steeply for the latter. This conclusion is arrived at after comparing the analytical expressions derived for both methods. Increasing the size of the projected subspace and using a cubic lattice with repetition coding is shown to be a possible solution to cope with cropping while retaining host interference cancellation.

Security of quantization index modulation (QIM) watermarking methods is usually sought through a pseudorandom dither signal which randomizes the codebook. This dither plays the role of the secret key (i.e., a parameter only shared by the watermarking embedder and decoder), which prevents unauthorized embedding and/or decoding. However, if the same dither signal is reused, the observation of several watermarked signals can provide sufficient information for an attacker to estimate the dither signal. This paper focuses on the cases when the embedded messages are either known or constant. In the first part of this paper, a theoretical security analysis of QIM data hiding measures the information leakage about the secret dither as the mutual information between the dither and the watermarked signals. In the second part, we show how set-membership estimation techniques successfully provide accurate estimates of the dither from observed watermarked signals. The conclusion of this twofold study is that current QIM watermarking schemes have a relative low security level against this scenario because a small number of observed watermarked signals yields a sufficiently accurate estimate of the secret dither. The analysis presented in this paper also serves as the basis for more involved scenarios

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.

The performance of watermarking methods based on lattice quantization schemes can be greatly degraded by simple amplitude scaling attacks. Scaling the amplitude of pixel values by relatively small amounts have the potential effect of moving the watermark vector away from its original quantization centroid, thus leading the decoder to incur erroneous decisions. In order to overcome this limitation, angle quantization index modulation (AQIM) schemes have been recently introduced. By quantizing the angle of the watermark vector according to a symbol dependent lattice, AQIM's construction leads to an inherent invariance against amplitude scaling distortions. In this paper, we proceed with a thorough theoretical analysis of the two-dimensional version of AQIM, leading to an exact expression for the bit error probability for simultaneous amplitude scaling and AWGN attacks. Such theoretical expressions were validated by comparison with experimental results, which are also included in this paper.

Quantization index modulation (QIM) watermarking has received a great deal of attention ever since the rediscovery of Costa's result on codes with host-interference rejecting properties. While such embedding schemes exhibit considerable improvement in watermark capacity over their earlier predecessors, (e.g. spread-spectrum), their fragility to even the simplest attacks soon became apparent. Among such attacks, amplitude scaling has received special attention. We introduce a quantization scheme, named angle QIM (AQIM), that is provably insensitive to amplitude scaling attacks. Instead of embedding information by quantizing the amplitude of pixel values, AQIM works by quantizing the angle formed by the host-signal vector with the origin of a hyperspherical coordinate system. Hence, AQIM's invariance to amplitude scaling can be shown by construction. Experimental results are presented for the bit error rate performance of AQIM under additive white Gaussian noise attacks.

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.

A novel quantization-based data-hiding method, named rational dither modulation (RDM), is presented. This method amounts to simple modifications of the well-known dither modulation (DM) scheme, which is largely vulnerable to scaling attacks. With such modifications, RDM becomes invariant to those attacks. Since RDM does not work by trying to estimate the step-size of the quantizers, it does not need any pilot-sequence. Moreover, RDM is suitable for a scalar operation, thus avoiding the cumbersome constructions of spherical codes. It is also shown that RDM approaches the performance of DM asymptotically with the size of the memory needed for the method to operate. Simulation results show the accuracy of our theoretical analysis and the superiority of RDM compared to the improved spread spectrum method.

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 this paper, we analyze spread-spectrum and quantization projection data hiding methods from a game-theoretic point of view, using the bit error rate (BER) as the payoff, and assuming that the embedder simply follows point-by-point constraints given by a perceptual mask, whereas for the attacker an MSE-like constraint is imposed. The optimal attacking and decoding strategies are obtained by making use of a theorem that in addition states that those strategies constitute an equilibrium of the game. Experimental results supporting our analyses are also shown.

In this paper we study the effects of synchronization errors on the bit error rate performance of spatial domain image watermarking. For that, we introduce the concept of the random jitter attack. This attack is characterized by the displacement of each pixel position by a random amount given by an arbitrary distribution, followed by interpolation over the modified sampling grid. An analogy is made between this attack and the errors introduced by timing jitter during sampling. Under such an analogy, a channel model is proposed to describe the effects of the jitter attack on the watermarked image. This model suggests that the effects of the jitter attack can be analyzed as the addition of signal dependent noise to the watermarked image. We derive expressions that relate the strength of the jitter attack to the bit error rate obtained for watermark decoding. These expressions are compared to experimental results obtained by performing the random jitter attack over a spatial domain spread spectrum watermark.

The binary distortion compensated dither-modulation (DC-DM), which can be regarded to as a baseline for quantization-based data-hiding methods, is rigorously analyzed. A novel and accurate procedure for computing the exact probability of bit error is given, as well as an approximation amenable to differentiation which allows to obtain the optimal weights in a newly proposed decoding structure, for significant improvements on performance. The results are particularized for a JPEG compression scenario which allows to show their usefulness. Experimental results validating the proposed theory are presented.

We provide an analysis of the constant modulus (CM) cost function under the assumption of a white equalizer input. This can be achieved by means of an adaptive prewhitening all-pole filter and has been suggested in previous works as a means for both MSE improvement and DFE cold start-up. For white inputs, it is seen that CM -optimizing the spherical component of the equalizer parameter vector is equivalent to minimizing the fourth moment of its output, regardless of the value of the radial component. This leads to an eigenvector interpretation of prewhitened CM receivers, and to a blind initialization procedure from an eigenvector of the quadri-covariance matrix of the whitened data. Connections with iterative eigenvector-based schemes are also explored.

A novel car speed estimation technique from a pair of omnidirectional microphones is presented. This approach is based on the maximum likelihood principle and it directly estimates the speed without any assumptions on the acoustic signal emitted by the vehicle. This has the advantages of bypassing troublesome intermediate delay estimation steps as well as eliminating the need for an accurate yet general enough acoustic model. An analysis of the estimate for the particular case of a narrowband source is provided. The estimation algorithm is well suited to DSP implementation and performs well with preliminary field data.

We propose a novel data hiding procedure called quantized projection (QP), that combines elements from quantization (i.e. quantization index modulation, QIM) and spread-spectrum methods. The method is based on quantizing a diversity projection of the host signal, inspired in the statistic used for detection in spread-spectrum algorithms. We carry out a theoretical analysis of QP together with its empirical validation to show rigorously that it offers an excellent performance; QP features probabilities of decoding error several orders of magnitude lower than the aforementioned families of methods for the same dimensionality (diversity) and attacking distortion level. In addition, we introduce a Costa-based improvement (see Costa, M.H.M., IEEE Trans. on Inf. Theory, vol.29, no.3, p.439-41, 1983) of the basic QP method named distortion compensated QP.

It has been claimed that quantization-based data-hiding methods offer an advantage over spread-spectrum schemes. In this paper we review this assertion by presenting a new look on the assumptions made for these claims, and we give a new performance comparison between both approaches under random additive channel distortions. The existence of a threshold in the distortion level for the goodness of each of the methods is shown.

The quantization of a linear projective transformation first proposed by Chen and Wornell is shown to allow for much better performance figures than those yielded by previous approaches. The procedure to achieve this improvement is explained through the proposal and analysis of an improved data hiding method called quantized projection (QP), based in the quantization of a statistic similar to those used at detection in spread-spectrum algorithms. Both the theoretical analysis and the empirical validation show that projection-based methods exhibit huge performance improvements over existing ones under the same conditions - i.e. same degree of diversity and level of random additive attacking distortion.

We take a new look to adaptive IIR lattice filters in order to devise algorithms that preserve the stability of the corresponding direct form schemes. By analyzing the local properties of stationary points, a transformation achieving this goal is suggested, which gives algorithms that can be efficiently implemented. Application to the Steiglitz-McBride and SHARF algorithms is presented. By contrast, it is shown that previous lattice versions of these algorithms may fail to preserve stability of stationary points.

We present an upper bound on the BER performance of the DVB-T system for time-invariant channels. A unified approach is taken comprising all feasible combinations of convolutional encoder rates and constellations, for both non-hierarchical and hierarchical transmission modes. The validity of the estimated BER is compared thoroughly with the simulated BER obtained with BerbeX, a DVB-T compliant software, for the three channels specified in the DVB-T standard.

In this paper, a spread-spectrum-like discrete cosine transform (DCT) domain watermarking technique for copyright protection of still digital images is analyzed. The DCT is applied in blocks of 8x8 pixels, as in the JPEG algorithm. The watermark can encode information to track illegal misuses. For flexibility purposes, the original image is not necessary during the ownership verification process, so it must be modeled by noise. Two tests are involved in the ownership verification stage: watermark decoding, in which the message carried by the watermark is extracted, and watermark detection, which decides whether a given image contains a watermark generated with a certain key. We apply generalized Gaussian distributions to statistically model the DCT coefficients of the original image and show how the resulting detector structures lead to considerable improvements in performance with respect to the correlation receiver, which has been widely considered in the literature and makes use of the Gaussian noise assumption. As a result of our work, analytical expressions for performance measures, such as the probability of errors in watermark decoding and the probabilities of false alarms and of detection in watermark detection, are derived and contrasted with experimental results.

This work considers the implementation of recursive identification algorithms based on hyperstability concepts with polyphase structures. It is shown that the strictly positive real (SPR) condition required for convergence of these schemes can always be met by using a sufficiently high polyphase expansion factor M. For a given M, the degree of persistent excitation required for parameter convergence is obtained. When a priori knowledge about the unknown system is available, a compensating filter can be designed to avoid the need for a high M.

In this paper, we address the problem of the performance analysis of image watermarking systems that do not require the availability of the original image during ownership verification. We focus on a statistical approach to obtain models that can serve as a basis for the application of decision theory to the design of efficient detector structures. Special attention is paid to the possible nonexistence of a statistical description of the original image. Different modeling approaches are proposed for the cases when such a statistical characterization is known and when it is not. Watermarks may encode a message, and the performance of the watermarking system is evaluated using as a measure the probability of false alarm, the probability of detection when the presence of the watermark is tested, and the probability of error when the information that it carries is extracted. Finally, the modeling techniques studied are applied to the analysis of two watermarking schemes, one of them defined in the spatial domain, and the other in the direct cosine transform (DCT) domain. The theoretical results are contrasted with empirical data obtained through experimentation covering several cases of interest. We show how choosing an appropriate statistical model for the original image can lead to considerable improvements in performance.

It is shown that the offline Steiglitz-McBride (1965) identification method may present stable limit cycles and even chaotic behavior when implemented in lattice form. Moreover, we also show that even under ideal conditions (i.e., with an adaptive filter of sufficient order and with noiseless measurements), the stationary point corresponding to identification of the unknown system need not be a convergent point of the lattice version of the online SM algorithm.

In this paper we analyze the effect that the application of channel coding produces on the performance of the watermark detection and decoding tests for copyright protection of images. Detector structures are derived for both tests and analytical bounds and approximations are obtained for the bit error rate (BER) and the receiver operating characteristic (ROC) associated with the watermark decoding and detection tests when block codes are employed. The extension to other families of codes is discussed. Finally, the analytical expressions are contrasted with experimental results in several cases of interest.

This paper is concerned with systems in which the output error of an adaptive IIR filter is subsequently filtered, e.g., an active noise control system where a transfer function models the path between the injection point of the cancellation noise and the point where the residual error is measured. We propose a family of algorithms suited to this type of scenarios, deriving conditions for their deterministic convergence. The analysis of the convergence is particularized to the filtered-U recursive LMS algorithm, a popular scheme whose global convergence has never been proved formally. Finally, some results based on real measurements are also presented.

A watermarking scheme for copyright protection of still images is modeled and analyzed. In this scheme a signal following a key-dependent two-dimensional multipulse modulation is added to the image for ownership enforcement purposes. The main contribution of this paper is the introduction of an analytical point of view to the estimation of performance measurements. Two topics are covered in the analysis: the ownership verification process, also called watermark detection test, and the data hiding process. In the first case, bounds and approximations to the receiver operating characteristic are derived. These results can be used to determine the threshold associated to a required probability of false alarm and the corresponding probability of detection. The data hiding process is modeled as a communications system and approximations for the bit error rate are derived. Finally, analytical expressions are contrasted with experimental results.