nils-herrmann/clean_scirepeval huggingface dataset:
Fingerprinting a flow of messages to an anonymous server

We present an attack to locate hidden servers in anonymous common networks. The attack is based on correlating the flow of messages that arrives to a certain server with the flow that is created by the attacker client. The fingerprint is constructed by sending requests, each request determines one interval. To improve the performance a prediction of the time of arrival is done for each request. We propose an optimal detector to decide whether the flow is fingerprinted, based on the Neyman-Pearson lemma. The usefulness of our algorithm is shown for the case of locating a Tor Hidden Service (HS), where we analytically determine the parameters that yield a fixed false positive probability and compute the corresponding detection probability. Finally, we empirically validate our results with a simulator and with a real implementation on the live Tor network. Results show that our algorithm outperforms any other flow watermarking scheme. Our design also yields a small detectability.
Meet the family of statistical disclosure attacks

Disclosure attacks aim at revealing communication patterns in anonymous communication systems, such as conversation partners or frequency. In this paper, we propose a framework to compare between the members of the statistical disclosure attack family. We compare different variants of the Statistical Disclosure Attack (SDA) in the literature, together with two new methods; as well as show their relation with the Least Squares Disclosure Attack (LSDA). We empirically explore the performance of the attacks with respect to the different parameters of the system. Our experiments show that i) our proposals considerably improve the state-of-the-art SDA and ii) confirm that LSDA outperforms the SDA family when the adversary has enough observations of the system.
Prefilter design for forensic resampling estimation

Starting from a theoretical analysis of the resampling estimation problem for image tampering detection, this work presents a study, based on cyclostationarity theory, about the use of prefilters to improve the estimation accuracy of the resampling factor. Considering the methods that perform the estimation by analyzing the spectrum of the covariance of a resampled region, we propose an analytical framework that allows the definition of a cost function that measures the degree of detectability of the spectral peaks. Based on this measure, the design of the optimum prefilters for a particular resampling factor can be solved numerically. Experimental results validate the developed analysis and illustrate the enhancement of the performance in a real scenario.
An accurate analysis of scalar quantization-based data hiding

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.
Adaptive lattice IIR filtering revisited: convergence issues and new algorithms with improved stability properties

Several algorithms for adaptive IIR filters parameterized in lattice form can be found in the literature. The salient feature of these structures when compared with the direct form is that ensuring stability is extremely easy. On the other hand, while computing the gradient signals that drive the direct form update algorithms is straightforward, it is not so for the lattice algorithms. This has led to simplified lattice algorithms using gradient approximations. Although, in general, these simplified schemes present the same stationary points as the original algorithms, whether this is also true for convergent points has remained an open problem. This also applies to nongradient-based lattice algorithms such as hyperstability based and the Steiglitz-McBride algorithms. Here, we answer this question in the negative, by showing that for several adaptive lattice algorithms, there exist settings in which the stationary point corresponding to identification of the unknown system is not convergent. In addition, new lattice algorithms with properties are derived. They are based on the cascade lattice structure, which allows the derivation of sufficient conditions for local stability.
Data Hiding Robust to Mobile Communication Vocoders

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.
Secure Adaptive Filtering

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.
Coping with the enemy: Advances in adversary-aware signal processing

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.
Skewed log-stable model for natural images pixel block-variance

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.
The impact of channel coding on the performance of spatial watermarking for copyright protection

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.
An exact expression for the bit error probability in angle QIM watermarking under simultaneous amplitude scaling and AWGN attacks

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.
Near optimal detection of quantized signals and application to JPEG forensics

In this paper, we investigate the problem of deciding whether a multidimensional signal has been quantized according to a given lattice or not. Under an infinite variance assumption, we derive the expression of the optimal detector, together with a practical approximation formula based on multidimensional Fourier series. As a forensic case study, the proposed detector is applied to the detection of nonaligned double JPEG compression. Results on both synthetic signals and real JPEG images show interesting properties of the proposed detector. Namely, the detector outperforms existing state-of-art detectors for nonaligned double JPEG compression. The application of the proposed scheme to other forensic problems seems a natural extension of this work.
Worst case additive attack against quantization-based watermarking techniques

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.
Security of Lattice-Based Data Hiding Against the Watermarked-Only Attack

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.
An extended analysis of discrete fourier tranform - rational dither modulation for non-white hosts

A deep analysis of discrete Fourier transform - rational dither modulation (DFT-RDM), developed to cope with linear time invariant filtering, is here presented. This study has been motivated by the significant increase of the error probability suffered by DFT-RDM for non-white hosts, in spite of the low bit-error rate achieved for white hosts under the same attack filter. The theoretical analysis of DFT-RDM has been generalized to non-white Gaussian hosts providing an explanation to the measured performance degradation. Afterwards an extension of DFT-RDM, named whitened DFT-RDM, is proposed to achieve the same bit-error rates for white and non-white Gaussian hosts. Experimental results are also presented that validate the developed analysis and illustrate the performance enhancement provided by the proposed extension of DFT-RDM, with both non-white Gaussian signals and real audio clips.
A generalization of Benford's law and its application to images

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 forensics, namely, detecting that a given image carries a hidden message.
Methodology for decisions making in transportation logistics function

The following article presents a review on the state of the art five basic criteria that a decision maker in the Supply Chain (SC) should consider in selecting carriers for the supply channel with suppliers and for channel distribution with distributors: opportunity, flexibility, cost savings, efficiency / productivity and technology. These are validated to a case study of SC Colombian green coffee, through the technical outranking multicriteria decision (ELECTRE), which seeks Carriers Selection (CS) under criteria. One of the contributions of this research is the methodology for decision making from building criteria from the literature to the application of ELECTRE technique. The second contribution to the decision maker in any SC is that he could take the code developed in Matlab and run at any time as making support organizational, logistical or production decisions, for any problem of selection, classification and prioritization.
Spread-Spectrum Watermarking Security

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.
The effect of the random jitter attack on the bit error rate performance of spatial domain image watermarking

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.
Modeling magnitudes of Gabor coefficients: The β-Rayleigh distribution

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 ¿ß norms 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.
Efficient reinitialization of the prewhitened constant modulus algorithm

We present a reinitialization scheme for blind equalizers adapted via the constant modulus algorithm (CMA) when an all-pole prefilter is included to whiten the received signal. The mechanism exploits the special structure of the minimum mean squared error (MMSE) equalizers and their relation with CMA equalizers. A heuristic rule for blind determination of the best equalization delay is also provided. Using these guidelines, the equalizer is capable of finding the optimal setting in an online and computationally efficient fashion. In particular, estimation and inversion of the channel output autocorrelation matrix is not needed, in contrast with previous approaches.
Overlay spectrum reuse in a multicarrier broadcast network: Single receiver analysis

The overlay cognitive radio paradigm presents a framework where a secondary user exploits the knowledge of the primary user's message to improve spectrum utilization. A multicarrier broadcast network is one of the scenarios where this knowledge is possible: the secondary user could join a single frequency network and, therefore, gain access to the primary message. However, if the primary signal is received with a strong line of sight component, its relaying from the secondary transmitter does not suffice to ensure the primary user quality of service. In this paper we study the scenario where a secondary transmitter maximizes its own transmission rate, keeping the quality of a primary receiver over a given threshold. The analytical results, based on bit error rate bounds, are verified by means of software simulations and hardware tests.
Number Theoretic Transforms for Secure Signal Processing

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.
Turbo coding for sample-level watermarking in the DCT domain

Coding at the sample level in still image watermarking takes advantage of avoiding a non-optimum initial diversity stage, used in many watermarking systems for tailoring Gaussian channels with desired properties. Nevertheless, the low signal-to-noise ratio (SNR) encountered, due to the perceptual constraints imposed to the watermark, makes necessary the use of very powerful coding techniques as it happens in deep-space communications.We study the application of turbo coding to sample-level discrete cosine transform (DCT) domain watermarking. This near-optimum codification strategy is possible thanks to the existence of statistical models in the DCT domain, that also permit the computation of theoretical bounds for performance.