Learning deep super-resolution models without the need for ground truth data at a higher resolution is critical for satellite imaging applications. This is either due to the lack of existing images at better resolution for certain target wavelengths or the existence of significant domain gaps between the images of different satellites. In this paper, we propose a method and neural network architecture for a multi-image super-resolution problem, where each image in the input stack might be affected by a different degradation process. A test-time finetuning procedure allows to dynamically account for the degradations observed for a specific set of LR inputs, improving over baseline results.

AI-based compression is gaining popularity for traditional photos and videos. However, such techniques do not typically scale well to the task of compressing hyperspectral images, and may have computational requirements in terms of memory usage and total floating point operations that are prohibitive for usage onboard of satellites. In this paper, we explore the design of a predictive compression method based on a novel neural network design, called LineRWKV. Our neural network predictor works in a line-by-line fashion limiting memory and computational requirements thanks to a recurrent inference mechanism. However, in contrast to classic recurrent networks, it relies on an attention operation that can be parallelized for training, akin to Transformers, unlocking efficient training on large datasets, which is critical to learn complex predictors. In our preliminary results, we show that LineRWKV significantly outperforms the state-of-the-art CCSDS-123 standard and has competitive throughput.

Abstract Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive sensing (CS) to reconstruct measurements in the original domain. Our lightweight design enforces the properties of unlinkability, revocability, and non-invertibility of the templates while preserving face recognition accuracy. We compare facial features by learning a regularized metric: at training time, we jointly learn facial features and the metric such that matching and non-matching pairs are mapped onto latent target distributions; then, for biometric verification, features are randomly projected via random matrices changed at every enrollment and query and reconstructed before the latent space mapping is computed. We assess the face recognition accuracy of our framework on challenging datasets such as LFW, CALFW, CPLFW, AgeDB, YTF, CFP, and RFW, showing notable improvements over state-of-the-art techniques while meeting the criteria for secure cancelable template design. Since our method requires no fine-tuning of the learned features, it can be applied to pre-trained networks to increase sensitive data protection.

Deep learning methods have traditionally been difficult to apply to compression of hyperspectral images onboard spacecrafts due to the large computational complexity needed to achieve adequate representational power, as well as the lack of suitable datasets for training and testing. In this article, we depart from the traditional autoencoder approach, and we design a predictive neural network, called line receptance weighted key value (LineRWKV), which works recursively line by line to limit memory consumption. In order to achieve that, we adopt a novel hybrid attentive-recursive operation that combines the representational advantages of Transformers with the linear complexity and recursive implementation of recurrent neural networks (RNNs). The compression algorithm performs the prediction of each pixel using LineRWKV, followed by entropy coding of the residual. Experiments on multiple datasets show that LineRWKV is highly memory-efficient, significantly outperforms state-of-the-art deep learning methods, and is the first deep learning approach to outperform CCSDS-123.0-B-2 at lossless and near-lossless compression. Promising throughput results are also evaluated on a 7-W embedded system.

In this paper, we explore the onboard processing capabilities of an optical Earth observation instrument operating under the principles of compressed sensing, currently under preliminary study. In particular, we focus on two main aspects for onboard operations: i) how to process measurements in a computationally-efficient way to obtain previews of the reconstructed image that can be easily used by downstream inference algorithms; ii) the possibility of having simultaneous compression and encryption by proper management of the pseudorandom patterns used for the sensing matrix and measurements

Compressive sensing has established itself as a novel imaging paradigm. In this paper, we analyze the behavior of a a compressive instrument based on spatial light modulators (SLM), operating in the mid-infrared. We show that, contrary to the well-studied visible and near-infrared wavelengths, mid-infrared poses modeling challenges due to non-negligible SLM diffraction effects. We show a way to model such effect analytically and to account for them in the reconstruction process, leading to improved reconstruction quality.

Multi-temporal super-resolution (SR) whereby a number of images of the same scene acquired at different times are fused to enhance its spatial resolution has recently enjoyed great success thanks to advances in deep learning methods. However, the literature has so far focused on supervised training approaches that require the availability of high-resolution (HR) images at the target resolution. This is a significant limitation because such imagery may not exist, might be difficult to source or exhibit domain gaps such as different spectral bands or radiometric characteristics. Unsupervised training approaches that do not require imagery beyond the input low resolution are needed to overcome this limitation. This paper presents a first analysis of the problem, taking inspiration from the literature on blind single-image SR, but also focusing on the uniqueness of multi-temporal satellite images. Our preliminary results show that it is indeed possible to develop accurate deep learning models for multi-temporal SR without HR images.

Abstract In this work, we present the Gaussian Class-Conditional Simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that improves over the state-of-the-art in terms of classification accuracy and adversarial robustness, with little extra cost for network training. The proposed method learns a mapping of the input classes onto Gaussian target distributions in a latent space such that a hyperplane can be used as the optimal decision surface. Instead of maximizing the likelihood of target labels for individual samples, our loss function pushes the network to produce feature distributions yielding high inter-class separation and low intra-class separation. The mean values of the learned distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy. Moreover, GCCS provides improved robustness against adversarial perturbations, outperforming models trained with conventional adversarial training (AT). In particular, our model learns a decision space that minimizes the presence of short paths toward neighboring decision regions. We provide a comprehensive empirical evaluation that shows how GCCS outperforms state-of-the-art approaches over challenging datasets for targeted and untargeted gradient-based, as well as gradient-free adversarial attacks, both in terms of classification accuracy and adversarial robustness.

PRNU (Photo-response non-uniformity) is widely considered a unique and reliable fingerprint for identifying the source of an image. The PRNU patterns of two different sensors, even if belonging to the same camera model, are strongly uncorrelated. Therefore, such a fingerprint is used as evidence by various law enforcement agencies for source identification, manipulation detection, etc. However, in recent smartphones, images are subjected to significant in-camera processing associated with computational photography. This heavy processing introduces non-unique artifacts (NUA) in such images and masks the uniqueness of the PRNU fingerprint. In this work, we investigate the robustness of PRNU in modern smartphones. We propose a model that explains the unexpected behavior of PRNU in such smartphones. Finally, we present two methods to identify images suffering from NUA. Our methods achieve high accuracy in identifying such images.

In modern society, the ever-increasing demand for Earth Observation products in a large variety of sectors is exposing the limitations of traditional satellite data chain architectures. The European Union Horizon 2020 EO-ALERT project aims at overcoming the existing bottlenecks by leveraging the performance of state-of-the-art commercial off-the-shelf devices to move the critical elements of data processing on the flight segment without sacrificing processing performance. This paper introduces the architecture of the EO-ALERT CPU Scheduling, Compression, Encryption and Data Handling Subsystem, responsible for coordinating the onboard optical and Synthetic Aperture Radar data chains, as well as providing data compression, encryption, and storage services. The performance obtained by a reference implementation of the proposed architecture is also presented, showing an extremely low contribution to the overall system latency that allows real-time Earth Observation product delivery to the end user in less than 5 min.

Due to a growing interest for imagery with high spatial and spectral resolution, Earth Observation sensors are producing increasing amounts of data. This poses a severe challenge in terms of computational, memory and transmission requirements. In order to overcome these limitations, a fascinating approach is the implementation of a compressive sensing architecture. In this paper, we present an instrumental concept based on the use of a spatial light modulator to implement a super-resolved, compressive demonstrator of an instrument aimed at Earth Observation in the visible and medium infrared spectral regions from geostationary platform.

Transmitting images from earth observation satellites to ground is a major challenge, and a compression/encryption stage is actually mandatory. Development of hardware accelerators is highly recommended, both to relieve the software from such demanding task, and to improve performance, aiming at quasi-real-time data processing. To this end, we discuss the design, development, deployment and test of a FPGA-based accelerator, featuring a lossless and lossy (near-lossless) compression, including the data encryption too. Its architecture is well suited for different image types, including single- and multi-band optical and SAR images and can be fully run-time configurable. Measured performance showed a throughput of 10 Msamples/s, in agreement with related state-of-the-art works, focused on lossless compression only.

Deep learning has shown outstanding performance in several applications including image classification. However, deep classifiers are known to be highly vulnerable to adversarial attacks, in that a minor perturbation of the input can easily lead to an error. Providing robustness to adversarial attacks is a very challenging task especially in problems involving a large number of classes, as it typically comes at the expense of an accuracy decrease. In this work, we propose the Gaussian class-conditional simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that provides adversarial robustness while at the same time achieving or even surpassing the classification accuracy of state-of-the-art methods. Differently from other frameworks, the proposed method learns a mapping of the input classes onto target distributions in a latent space such that the classes are linearly separable. Instead of maximizing the likelihood of target labels for individual samples, our objective function pushes the network to produce feature distributions yielding high inter-class separation. The mean values of the distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy and inherently provides robustness to multiple adversarial attacks, both targeted and untargeted, outperforming state-of-the-art approaches over challenging datasets.

Compressed sensing (CS) has recently emerged as an effective and efficient way to encrypt data. Under certain conditions, it has been shown to provide some secrecy notions. In theory, it could be considered to be a perfect match for constrained devices needing to acquire and protect the data with computationally cheap operations. However, the theoretical results on the secrecy of compressive cryptosystems only hold under the assumption of infinite precision representation. With this work, we aim to close this gap and lay the theoretical foundations to support this practical framework. We provide theoretical upper bounds on the distinguishability of the measurements acquired through finite precision sensing matrices and experimentally validate them. Our main result is that the secrecy of a CS cryptosystem can be exponentially increased with a linear increase in the representation precision. This result confirms that the CS can be an effective secrecy layer and provides tools to use it in practical settings.

This work presents a reduced complexity image clustering (RCIC) algorithm that blindly groups images based on their camera fingerprint. The algorithm does not need any prior information and can be implemented without and with attraction, to refine clusters. After a camera fingerprint is estimated for each image in the data set, a fingerprint is randomly selected as reference fingerprint and a cluster is constructed using this fingerprint as centroid. The clustered fingerprints are removed from the data set and the remaining fingerprints are clustered repeating the same process. A further attraction stage can be included, in which a similar algorithm is performed using the centroids of the clusters found after the first stage. Despite its simplicity, results show that RCIC algorithm has lower computational cost than existing algorithms, while maintaining similar or even better performance. Moreover, the performance of the proposed algorithm is not affected significantly when the number of cameras in the data set is much larger than the average number of images from each camera.

This work presents a new camera fingerprint-based image clustering algorithm. The proposed algorithm is based on sorting the camera fingerprints according to information that is inherently present in images. A ranking index is constructed for each image, taking into account the combined effect of gray-level, saturation and texture on camera fingerprint estimation. Then, camera fingerprints are ordered according to this ranking index and clusters are iteratively constructed using as reference fingerprint the top-ranked fingerprint among the currently un-clustered fingerprints. The algorithm can be optionally implemented with an additional attraction stage to refine clustering. The results confirm that the proposed method achieves a performance comparable to state of the art approaches, with a significantly lower computational complexity. The method can also handle cases in which the number of clusters is much larger than the average size of the clusters.

We propose a novel architecture for generic biometric authentication based on deep neural networks: RegNet. Differently from other methods, RegNet learns a mapping of the input biometric traits onto a target distribution in a well-behaved space in which users can be separated by means of simple and tunable boundaries. More specifically, authorized and unauthorized users are mapped onto two different and well behaved Gaussian distributions. The novel approach of learning the mapping instead of the boundaries further avoids the problem encountered in typical classifiers for which the learnt boundaries may be complex and difficult to analyze. RegNet achieves high performance in terms of security metrics such as Equal Error Rate (EER), False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR). The experiments we conducted on publicly available datasets of face and fingerprint confirm the effectiveness of the proposed system.

In recent years, the Compressed Sensing (CS) framework has been shown to be an effective private key cryptosystem. If infinite precision is available, then it has been shown that spherical secrecy can be achieved. However, despite its theoretically proven secrecy properties, the only practically feasible implementations involve the use of Bernoulli sensing matrices. In this work, we show that different distributions employing a much larger finite alphabet can be considered. More in detail, we consider the use of quantized Gaussian sensing matrices and experimentally show that, besides being suitable for practical implementation, they can achieve higher secrecy with respect to Bernoulli sensing matrices. Furthermore, we show that this approach can be used to tune the secrecy of the CS cryptosystems based on the available machine precision.

Multifactor user authentication systems enhance security by augmenting passwords with the verification of additional pieces of information such as the possession of a particular device. This paper presents an innovative user authentication scheme that verifies the possession of one’s smartphone by uniquely identifying its camera. High-frequency components of the photo-response nonuniformity of the optical sensor are extracted from raw images and used as a weak physical unclonable function. A novel scheme for efficient transmission and server-side verification is also designed based on adaptive random projections and on an innovative fuzzy extractor using polar codes. The security of the system is thoroughly analyzed under different attack scenarios both theoretically and experimentally.

In this paper, we propose a method for estimating the sparsity of a signal from its noisy linear projections without recovering it. The method exploits the property that linear projections acquired using a sparse sensing matrix are distributed according to a mixture distribution whose parameters depend on the signal sparsity. Due to the complexity of the exact mixture model, we introduce an approximate two-component Gaussian mixture model whose parameters can be estimated via expectation-maximization techniques. We demonstrate that the above model is accurate in the large system limit for a proper choice of the sensing matrix sparsifying parameter. Moreover, experimental results demonstrate that the method is robust under different signal-to-noise ratios and outperforms existing sparsity estimation techniques.

In this paper, the security of the compressed sensing (CS) framework as a form of data confidentiality is analyzed. Two important properties of one-time random linear measurements acquired using a Gaussian independent identically distributed matrix are outlined: 1) the measurements reveal only the energy of the sensed signal and 2) only the energy of the measurements leaks information about the signal. An important consequence of the above facts is that CS provides information theoretic secrecy in a particular setting. Namely, a simple strategy based on the normalization of the Gaussian measurements achieves, at least in theory, perfect secrecy, enabling the use of CS as an additional security layer in privacy preserving applications. In the generic setting in which CS does not provide information theoretic secrecy, two alternative security notions linked to the difficulty of estimating the energy of the signal and distinguishing equal-energy signals are introduced. Useful bounds on the mean square error of any possible estimator and the probability of error of any possible detector are provided and compared with the simulations. The results indicate that CS is in general not secure according to cryptographic standards, but may provide a useful built-in data obfuscation layer.

In this paper, we propose a mathematical model to estimate the sparsity degree k of exactly k-sparse signals acquired through Compressed Sensing (CS). Our method does not need to recover the signal to estimate its sparsity, and is based on the use of sparse sensing matrices. We exploit this model to propose a CS acquisition system where the number of measurements is calculated on-the-fly depending on the estimated signal sparsity. Experimental results on block-based CS acquisition of black and white images show that the proposed adaptive technique outperforms classical CS acquisition methods where the number of measurements is set a priori.

Recently, we demonstrated that random projections can provide an extremely compact representation of a camera fingerprint without significantly affecting the matching performance. In this paper, we propose a new construction that makes random projections of camera fingerprints scale-robust. The proposed method maps the compressed fingerprint of a rescaled image to the compressed fingerprint of the original image, rescaled by the same factor. In this way, fingerprints obtained from rescaled images can be directly matched in the compressed domain, which is much more efficient than existing scale-robust approaches. Experimental results on the publicly available Dresden database show that the proposed technique is robust to a wide range of scale transformations. Moreover, robustness can be further improved by providing reference scales in the database, with a small additional storage cost.

Image retrieval is the process of finding images from a large collection, satisfying a user-specified criterion. Content-based retrieval has been the traditional paradigm, in which one wishes to find images whose content is similar to a query. In this paper we explore a novel criterion for image search, based on forensic principles. We address the problem of retrieving all the photos in a collection that have been acquired by a specific device which is presented to the system as a query. This is an important forensic problem, whose solution could be very useful for detecting improper usage of pictures. We do not rely on metadata such as Exif headers because they can be unavailable, or easily manipulated, and in most cases cannot identify the specific device. We rely instead on a forensic tool called Photo Response Non-Uniformity (PRNU), which constitutes a reliable fingerprint of a camera sensor. We examine recent advances in compression of such fingerprints, which allow to address the previously unexplored image retrieval problem on large scales.

In this paper, we propose two different solutions for making a recently proposed asymmetric fingerprinting protocol based on client-side embedding robust to collusion attacks. The first solution is based on projecting a client-owned random fingerprint, securely obtained through existing cryptographic protocols, using for each client a different random matrix generated by the server. The second solution consists in assigning to each client a Tardos code, which can be done using existing asymmetric protocols, and modulating such codes using a specially designed random matrix. Suitable accusation strategies are proposed for both solutions, and their performance under the averaging attack followed by the addition of Gaussian noise is analytically derived. Experimental results show that the analytical model accurately predicts the performance of a realistic system. Moreover, the results also show that the solution based on independent random projections outperforms the solution based on Tardos codes, for different choices of parameters and under different attack models.

Retrieving pictures from large collections according to a specific criterion is an increasingly relevant task. An important , but so far overlooked, such criterion is the retrieval of pictures acquired by a specific camera. Instead of relying on metadata , which can be absent or easily manipulated, a forensic tool is exploited, namely the photo response non-uniformity (PRNU) of the camera sensor. Recent works showed that random projections can be used to significantly compress the PRNU, enabling operation on very large scales, previously impossible due to the size of the PRNU and to the complexity of the matching operations. In this paper, we propose efficient techniques for management and retrieval of images employing the PRNU, and test them on a database of 1174 cameras and half a million pictures downloaded from the Internet.

In this paper, we analyze the security of compressed sensing (CS) as a cryptosystem. We demonstrate that random linear measurements acquired using a Gaussian i.i.d. matrix reveal only the energy of the sensed signal, and that only the energy of the measurements leaks information about the signal. We provide useful bounds for assessing the information leakage about the energy, linking those bounds to the minimum mean square error achievable by practical estimators. Moreover, we propose a simple strategy based on the normalization of the measurements which achieves, at least in theory, perfect secrecy, enabling the use of CS-based encryption in practical cryptosystems.

In this paper, we propose a scheme to employ an asymmetric fingerprinting protocol within a client-side embedding distribution framework. The scheme is based on a novel client-side embedding technique that is able to transmit a binary fingerprint. This enables secure distribution of personalized decryption keys containing the Buyer's fingerprint by means of existing asymmetric protocols, without using a trusted third party. Simulation results show that the fingerprint can be reliably recovered by using non-blind decoding, and it is robust with respect to common attacks. The proposed scheme can be a valid solution to both customer's rights and scalability issues in multimedia content distribution.

We propose a method for detecting insertion and deletion of whole frames in digital videos. We start by strengthening and extending a state of the art method for double encoding detection, and propose a system that is able to locate the point in time where frames have been deleted or inserted, discerning between the two cases. The proposed method is applicable even when different codecs are used for the first and second compression, and performs well even when the second encoding is as strong as the first one.

Abstract In this work, by exploiting the traces left by double compression in the statistics of quantized modified discrete cosine transform coefficients, a single measure has been derived that allows to decide whether an MP3 file is singly or doubly compressed and, in the last case, to devise also the bit-rate of the first compression. Moreover, the proposed method as well as two state-of-the-art methods have been applied to analyze short temporal windows of the track, allowing the localization of possible tampered portions in the MP3 file under analysis. Experiments confirm the good performance of the proposed scheme and demonstrate that current detection methods are useful for tampering localization, thus offering a new tool for the forensic analysis of MP3 audio tracks.

In the past few decades, several methods have been developed for despeckling synthetic aperture radar (SAR) images. A considerable number of them have been derived under the assumption of a fully-developed speckle model in which the multiplicative speckle noise is supposed to be a white process. Unfortunately, the transfer function of SAR acquisition systems can introduce a statistical correlation, which decreases the despeckling efficiency of such filters. In this paper, a whitening method is proposed for processing a complex image acquired by a SAR system. We demonstrate that the proposed approach allows the successful application of classical despeckling algorithms. First, we perform an estimation of the SAR system frequency response based on some statistical properties of the acquired image and by using realistic assumptions. Then, a decorrelation process is applied on the acquired image, taking into account the presence of point targets. Finally, the image is despeckled. The experimental results show that the despeckling filters achieve better performance when they are preceded by the proposed whitening method; furthermore, the radiometric characteristics of the image are preserved.

In this paper, we propose a solution for implementing an asymmetric fingerprinting protocol within a client-side embedding distribution framework. The scheme is based on two novel client-side embedding techniques that are able to reliably transmit a binary fingerprint. The first one relies on standard spread-spectrum like client-side embedding, while the second one is based on an innovative client-side informed embedding technique. The proposed techniques enable secure distribution of personalized decryption keys containing the Buyer's fingerprint by means of existing asymmetric protocols, without using a trusted third party. Simulation results show that the fingerprint can be reliably recovered by using either nonblind decoding with standard embedding or blind decoding with informed embedding, and in both cases it is robust with respect to common attacks. To the best of our knowledge, the proposed scheme is the first solution addressing asymmetric fingerprinting within a client-side framework, representing a valid solution to both customer's rights and scalability issues in multimedia content distribution.

In this paper, a method to detect the presence of double compression in a MP3 audio file is proposed. By exploiting the effect of double compression in the statistical properties of quantized MDCT coefficients, a single measure is derived to decide if a MP3 file is single compressed or it has been double compressed and also to devise the bit-rate of the first compression. Experimental results confirm the performance of the detector, mainly when the bit-rate of the second compression is higher than the bit-rate of the first one.

A comparison between two forensic techniques for the reverse engineering of a chain composed by a double JPEG compression interleaved by a linear contrast enhancement is presented here. The first approach is based on the well known peak-to-valley behavior of the histogram of double-quantized DCT coefficients, while the second approach is based on the distribution of the first digit of DCT coefficients. These methods have been extended to the study of the considered processing chain, for both the chain detection and the estimation of its parameters. More specifically, the proposed approaches provide an estimation of the quality factor of the previous JPEG compression and the amount of linear contrast enhancement.

This work addresses forgery localization in MPEG-2 compressed videos. The proposed method is based on the analysis of Double Quantization (DQ) traces in frames that were encoded twice as intra (i.e., I-frames). Employing a state-of-the-art method, such frames are located in the video under analysis by estimating the size of the Group Of Pictures (GOP) that was used in the first compression; then, the DQ analysis is devised for the MPEG-2 encoding scheme and applied to frames that were intra-coded in both the first and second compression. In such a way, regions that were manipulated between the two encodings are detected. Compared to existing methods based on double quantization analysis, the proposed scheme makes forgery localization possible on a wider range of settings.

In this work, we present a decision fusion strategy for image forensics. We define a framework that exploits information provided by available forensic tools to yield a global judgment about the authenticity of an image. Sources of information are modeled and fused using Dempster-Shafer Theory of Evidence, since this theory allows us to handle uncertain answers from tools and lack of knowledge about prior probabilities better than the classical Bayesian approach. The proposed framework permits us to exploit any available information about tools reliability and about the compatibility between the traces the forensic tools look for. The framework is easily extendable: new tools can be added incrementally with a little effort. Comparison with logical disjunction- and SVM-based fusion approaches shows an improvement in classification accuracy, particularly when strong generalization capabilities are needed.

This paper describes the most recent achievements in speckle reduction of COSMO-SkyMed (CSK@) synthetic aperture radar (SAR) data. An advanced multiresolution despeckling filter, based on undecimated wavelet transform (UDWT) and maximum a-posteriori (MAP) estimation has been specialized and optimized to CSKê data, both single- and multi-look. The tradeoff between performances and computational complexity has been investigated: Laplacian-Gaussian and generalized Gaussian (GG) priors for MAP estimation in UDWT domain differ by one order of magnitude in computation cost. Pre-processing of point targets and segmentation of wavelet planes has been exploited to effectively handle the heterogeneity of the data. Besides traditional supervised methods to evaluate the quality of despeckling, a novel procedure, fully automated, based on bivariate analysis of noisy and denoised image has been devised.

The undecimated wavelet transform and the maximum a posteriori probability (MAP) criterion have been applied to the problem of synthetic-aperture-radar image despeckling. The MAP solution is based on the assumption that wavelet coefficients have a known distribution. In previous works, the generalized Gaussian (GG) function has been successfully employed. Furthermore, despeckling methods can be improved by using a classification of wavelet coefficients according to their texture energy. A major drawback of using the GG distribution is the high computational cost since the MAP solution can be found only numerically. In this letter, a new modeling of the statistics of wavelet coefficients is proposed. Observations of the estimated GG shape parameters relative to the reflectivity and to the speckle noise suggest that their distributions can be approximated as a Laplacian and a Gaussian function, respectively. Under these hypotheses, a closed form solution of the MAP estimation problem can be achieved. As for the GG case, classification of wavelet coefficients according to their texture content may be exploited also in the proposed method. Experimental results show that the fast MAP estimator based on the Laplacian-Gaussian assumption and on the classification of coefficients reaches almost the same performances as the GG version in terms of speckle removal, with a gain in computational cost of about one order of magnitude.

In this paper, a simple yet reliable algorithm to detect the presence of nonaligned double JPEG compression (NA-JPEG) in compressed images is proposed. The method evaluates a single feature based on the integer periodicity of the blockwise discrete cosine transform (DCT) coefficients when the DCT is computed according to the grid of the previous JPEG compression. Even if the proposed feature is computed relying only on DC coefficient statistics, a simple threshold detector can classify NA-JPEG images with improved accuracy with respect to existing methods and on smaller image sizes, without resorting to a properly trained classifier. Moreover, the proposed scheme is able to accurately estimate the grid shift and the quantization step of the DC coefficient of the primary JPEG compression, allowing one to perform a more detailed analysis of possibly forged images.

In this paper, we propose a forensic algorithm to discriminate between original and forged regions in JPEG images, under the hypothesis that the tampered image presents a double JPEG compression, either aligned (A-DJPG) or nonaligned (NA-DJPG). Unlike previous approaches, the proposed algorithm does not need to manually select a suspect region in order to test the presence or the absence of double compression artifacts. Based on an improved and unified statistical model characterizing the artifacts that appear in the presence of both A-DJPG or NA-DJPG, the proposed algorithm automatically computes a likelihood map indicating the probability for each 8 × 8 discrete cosine transform block of being doubly compressed. The validity of the proposed approach has been assessed by evaluating the performance of a detector based on thresholding the likelihood map, considering different forensic scenarios. The effectiveness of the proposed method is also confirmed by tests carried on realistic tampered images. An interesting property of the proposed Bayesian approach is that it can be easily extended to work with traces left by other kinds of processing.

In this paper, a forensic tool able to discriminate between original and forged regions in an image captured by a digital camera is presented. We make the assumption that the image is acquired using a Color Filter Array, and that tampering removes the artifacts due to the demosaicking algorithm. The proposed method is based on a new feature measuring the presence of demosaicking artifacts at a local level, and on a new statistical model allowing to derive the tampering probability of each 2 × 2 image block without requiring to know a priori the position of the forged region. Experimental results on different cameras equipped with different demosaicking algorithms demonstrate both the validity of the theoretical model and the effectiveness of our scheme.

The undecimated wavelet transform and the maximum a posteriori (MAP) criterion have been applied to the problem of despeckling SAR images. The solution is based on the assumption that the wavelet coefficients have a known distribution. In previous works, the generalized Gaussian function has been successfully employed. In this case, a major problem is the computational cost, since the solution can be found only numerically. In this work, a different modeling is proposed. The observation of the experimental histograms of the wavelet coefficients related to the reflectivity and to speckle noise demonstrates that their distributions can be approximated as a Laplacian and a Gaussian function, respectively. Under these hypotheses, a closed form solution of the MAP estimation problem can be achieved. In addition, a closed form estimator based on the MMSE criterion also exists. The experimental results show that the fast MAP and MMSE estimators reach almost the same performances of their generalized Gaussian based counterparts in terms of speckle removal, with a computational gain of about one order of magnitude.

We propose a novel model to characterize the security of linear blinding techniques. The proposed model relates the security of blinding to the possibility of estimating the blinded signals up to a certain signal-to-noise ratio (SNR). Practical upper bounds on the SNR are derived by relying on rate-distortion theory and evaluating the mutual information between the blinded and the plaintext signals. The proposed bounds allow to characterize the security of different blinding techniques, showing that multiplicative blinding techniques can not achieve the same level of security as additive ones. The proposed model provides a rigorous measure for evaluating the tradeoff between security and efficiency in practical secure signal processing algorithms.

In this paper, we propose a statistical test to discriminate between original and forged regions in JPEG images, under the hypothesis that the former are doubly compressed while the latter are singly compressed. New probability models for the DCT coefficients of singly and doubly compressed regions are proposed, together with a reliable method for estimating the primary quantization factor in the case of double compression. Based on such models, the probability for each DCT block to be forged is derived. Experimental results demonstrate a better discriminating behavior with respect to previously proposed methods.

In this paper, we propose a simple yet reliable method to detect the presence of non-aligned double JPEG compression (NA-JPEG). The method is based on a single feature which depends on the integer periodicity of the DCT coefficients when the DCT is computed according to the grid of the previous JPEG compression. Even if the proposed feature is computed relying only on DC coefficient statistics, a simple threshold detector can classify NA-JPEG images with improved accuracy with respect to existing methods and on smaller image sizes. Moreover, the proposed method is able to accurately estimate the quantization step and the grid shift of the primary JPEG compression, which can be used to perform a more detailed analysis of possibly forged images.

In this work, maximum a posteriori (MAP) despeckling, implemented in the multiresolution domain defined by the undecimated discrete wavelet transform (UDWT), will carried out on very high resolution (VHR) SAR images and compared with earlier multiresolution approaches developed by the authors. The MAP solution in UDWT domain has been specialized to SAR imagery. Every UDWT subband is segmented into statistically homogeneous segments and one generalized Gaussian (GG) PDF (variance and shape factor) is estimated for each segment. This solution allows to effectively handle scene heterogeneity as imaged by the VHR SAR system. Segmentation exploits a Tree Structured Markov Random Field (TSMRF), which is a low complexity MRF segmentation that allows the estimation of the number of segments and the segmentation itself to be carried out at same time. Experiments performed on a single-look VHR X-band SAR images demonstrate that the segmented approach is effective whenever the classical circular Gaussian model of complex reflectivity may no longer hold.

Signal processing tools working directly on encrypted data could provide an efficient solution to application scenarios where sensitive signals must be protected from an untrusted processing device. In this paper, we consider the data expansion required to pass from the plaintext to the encrypted representation of signals, due to the use of cryptosystems operating on very large algebraic structures. A general composite signal representation allowing us to pack together a number of signal samples and process them as a unique sample is proposed. The proposed representation permits us to speed up linear operations on encrypted signals via parallel processing and to reduce the size of the encrypted signal. A case study-1-D linear filtering-shows the merits of the proposed representation and provides some insights regarding the signal processing algorithms more suited to work on the composite representation.

Client-side watermark embedding systems have been proposed as a possible solution for the copyright protection in large-scale content distribution environments. In this framework, we propose a new look-up-table-based secure client-side embedding scheme properly designed for the spread transform dither modulation watermarking method. A theoretical analysis of the detector performance under the most known attack models is presented and the agreement between theoretical and experimental results verified through several simulations. The experimental results also prove that the advantages of the informed embedding technique in comparison to the spread-spectrum watermarking approach, which are well known in the classical embedding schemes, are preserved in the client-side scenario. The proposed approach permits us to successfully combine the security of client-side embedding with the robustness of informed embedding methods.

Buyer-seller watermarking (BSW) protocols allow copyright protection of digital content. The protocol is anonymous when the identity of buyers is not revealed if they do not release pirated copies. Existing BSW protocols are not provided with a formal analysis of their security properties. We employ the ideal-world/real-world paradigm to propose a formal security definition for copyright protection protocols, and we analyze an anonymous BSW protocol and prove that it fulfills our definition. Additionally, we implement the protocol and measure its efficiency.

Client side watermark embedding schemes have been proposed as a possible solution for the copyright protection in large scale content distribution environments. In this framework, we propose a look-up-table based secure embedding system, designed for the Spread Transform Dither Modulation (ST-DM) watermarking algorithm, that outperforms Spread Spectrum based systems.

Signal processing in the encrypted domain (s.p.e.d.) appears an elegant solution in application scenarios, where valuable signals must be protected from a possibly malicious processing device. In this paper, we consider the application of the Discrete Cosine Transform (DCT) to images encrypted by using an appropriate homomorphic cryptosystem. An s.p.e.d. 1-dimensional DCT is obtained by defining a convenient signal model and is extended to the 2-dimensional case by using separable processing of rows and columns. The bounds imposed by the cryptosystem on the size of the DCT and the arithmetic precision are derived, considering both the direct DCT algorithm and its fast version. Particular attention is given to block-based DCT (BDCT), with emphasis on the possibility of lowering the computational burden by parallel application of the s.p.e.d. DCT to different image blocks. The application of the s.p.e.d. 2D-DCT and 2D-BDCT to 8-bit greyscale images is analyzed; whereas a case study demonstrates the feasibility of the s.p.e.d. DCT in a practical scenario.

Signal-processing modules working directly on encrypted data provide an elegant solution to application scenarios where valuable signals must be protected from a malicious processing device. In this paper, we investigate the implementation of the discrete Fourier transform (DFT) in the encrypted domain by using the homomorphic properties of the underlying cryptosystem. Several important issues are considered for the direct DFT: the radix-2 and the radix-4 fast Fourier algorithms, including the error analysis and the maximum size of the sequence that can be transformed. We also provide computational complexity analyses and comparisons. The results show that the radix-4 fast Fourier transform is best suited for an encrypted domain implementation in the proposed scenarios.

The nonsubsampled contourlet transform (NSCT) is a powerful and versatile tool that allows a multiresolution and directional representation to be achieved. In this paper, we propose an extension of two despeckling algorithms, proposed to restore SAR images and based on the undecimated separable wavelet transform, to work into the NSCT domain. The signal is modeled as affected by a multiplicative noise. The noise-free NSCT coefficients are estimated from the observed ones according to either the maximum-a-posteriori (MAP) or the linear minimum mean square error (LMMSE) criterion. The results show that the proposed restoration algorithms highly benefit from the fact of working into a multiresolution and multidirectional domain.

Signal processing modules working directly on the encrypted data could provide an elegant solution to application scenarios where valuable signals should be protected from a malicious processing device. In this paper, we investigate the implementation of the discrete Fourier transform (DFT) in the encrypted domain, by using the homomorphic properties of the underlying cryptosystem. Several important issues are considered for both the DFT and radix-2 fast Fourier transform, including the error analysis and the maximum size of the sequence that can be transformed.

In this paper, we apply probabilistic data association (PDA) to the detection of asynchronous users in the uplink of a MC-CDMA system. It is shown that the detection can be performed by applying the PDA algorithm to two consecutive MC-CDMA symbols. Moreover, the PDA algorithm definition has been revisited in order to provide a more general and flexible implementation. Several versions of the PDA receiver have been proposed, which have been compared in terms of complexity and achievable bit error rate. The results show that the PDA approach achieves near optimal performance, while the algorithm flexibility can be exploited in order to obtain a reasonable complexity reduction.

Processing a signal directly in the encrypted domain provides an elegant solution in application scenarios where valuable signals must be protected from a malicious processing device. In a previous paper we considered the implementation of the ID discrete fourier transform (DFT) in the encrypted domain, by using the homomorphic properties of the underlying cryptosystem. In this paper we extend our previous results by considering the application of the 2-dimensional DCT to encrypted images. The effect of the consecutive application of the DCT algorithm first by rows then by columns is considered, as well as the differences between the implementation of the direct DCT algorithm and its fast version. Particular attention is given to block-based DCT, with emphasis on the possibility of lowering the computational burden by parallel application of the encrypted domain DCT algorithm to different image blocks.

In this paper, we propose an extension of two despeckling algorithms, proposed to denoise SAR images and based on the undecimated separable wavelet transform, to work with the nonsubsampled contourlet transform (NSCT). The NSCT is a powerful and versatile nonseparable transform that allows a multiresolution and directional representation to be achieved. The SAR signal is modeled as affected by a multiplicative noise. The noise-free NSCT coefficients are estimated from the observed ones according to either the maximum-a-posteriori (MAP) or the linear minimum mean square error (LMMSE) criterion. The results show that the proposed de-speckling algorithms highly benefit from the fact of working into a multiresolution and multidirectional domain.

In this paper, we propose an original detection scheme for high rate short-range impulse radio ultra-wideband systems. The proposed receiver relies on both the introduction of the cyclic prefix at the transmitter and the use of a frequency domain multiuser detector at the receiver. Zero forcing (ZF) and minimum mean square error (MMSE) detection strategies have been investigated and compared with the classical RAKE, considering a scenario where several mobile terminals communicate with a base station in an indoor environment characterized by severe multipath propagation. The results show that the MMSE receiver achieves the best performance, irrespective of the number of active terminals, both in the uplink and in the downlink communications. Hence, the proposed approach is well suited in indoor wireless environments where the multipath propagation tends to increase the effects of both the inter-path and the inter-user interference.

In this paper, a novel despeckling algorithm based on undecimated wavelet decomposition and maximumaposterioriestimation is proposed. Such a method represents an improvement with respect to the filter presented by the authors, and it is based on the same conjecture that the probability density functions (pdfs) of the wavelet coefficients follow a generalized Gaussian (GG) distribution. However, the approach introduced here presents two major novelties: 1) theoretically exact expressions for the estimation of the GG parameters are derived: such expressions do not require further assumptions other than the multiplicative model with uncorrelated speckle, and hold also in the case of a strongly correlated reflectivity; 2) a model for the classification of the wavelet coefficients according to their texture energy is introduced. This model allows us to classify the wavelet coefficients into classes having different degrees of heterogeneity, so thatadhocestimation approaches can be devised for the different sets of coefficients. Three different implementations, characterized by different approaches for incorporating into the filtering procedure the information deriving from the segmentation of the wavelet coefficients, are proposed. Experimental results, carried out on both artificially speckled images and true synthetic aperture radar images, demonstrate that the proposed filtering approach outperforms the previous filters, irrespective of the features of the underlying reflectivity.

In this paper, the performance of two different communication techniques for impulse radio UWB systems is compared: the techniques are based on the pulse repetition according to the spreading factor value and the cyclic prefix insertion. Since both techniques cause a throughput loss, they have to be compared both in terms of performance and capacity, i.e. the maximum data rate which is afforded. Different scenarios are taken into account: in an indoor short-range environment an access point communicates with several mobile terminals by transmitting low or high data-rate flows with variable system loads. Alternative detection strategies are considered, aiming at highlighting the most suitable approach.

In this paper, the performance of two different communication techniques for impulse radio UWB systems is compared: the techniques are based on the pulse repetition according to the spreading factor value and the cyclic prefix insertion. Since both techniques cause a throughput loss, they have to be compared both in terms of performance and capacity, i.e. the maximum data rate which is afforded. Different scenarios are taken into account: in an indoor short-range environment an access point communicates with several mobile terminals by transmitting low or high data-rate flows with variable system loads. Alternative detection strategies are considered, aiming at high-lighting the most suitable approach

The multiband UWB approach is well-seen as one of the most promising technique for short range communications due to its compatibility towards 802.11 family. In this contribution we aim to present a multiband UWB system using cross-polarized antenna, a channel model taking into account also the mutual orientation of Tx and Rx antennas and, at last, several time-frequency codes for accessing the channel in a multi-piconets environment

In this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori MIAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformation.

In this paper we propose an innovative communication scheme for ultra-wideband systems which are based on impulse radio. The proposed system relies on both the introduction of the cyclic prefix at the transmitter and the use of a frequency domain detector at the receiver. Two different detection strategies based either on the zero forcing (ZF) or the minimum mean square error (MMSE) criteria have been investigated and compared with the classical RAKE, considering two scenarios where a base station transmits to several mobile terminals in an indoor environment characterized by severe multipath propagation. The results show that the MMSE receiver achieves the best performance, irrespective of the number of active terminals

In this paper, we propose a detection strategy for high rate short-range impulse radio ultra-wideband systems. The proposed detection scheme relies on both the introduction of the cyclic prefix at the transmitter and the use of a frequency domain multiuser detection approach at the receiver. Zero forcing (ZF) and minimum mean square error (MMSE) multiuser detectors have been investigated and compared with the classical RAKE, considering a scenario where several mobile terminals communicate with a base station in an indoor environment characterized by severe multipath propagation. The results show that the MMSE receiver achieves the best performance, irrespective of the number of active terminals. Hence, the proposed detectors appear to be well suited for future indoor wireless applications

In this paper, we propose an original multiuser detector for high rate short-range impulse radio ultra-wideband systems. The proposed receiver relies on both the introduction of the cyclic prefix at the transmitter and the use of a frequency domain multiuser detector at the receiver. Zero forcing (ZF) and minimum mean square error (MMSE) detection strategies have been investigated and compared with the classical RAKE, considering a scenario where several mobile terminals transmit to a base station in an indoor environment characterized by severe multipath propagation. The results show that the MMSE receiver achieves the best performance, irrespective of the number of active terminals.

In this paper, we propose a detection strategy for high rate short-range impulse radio ultra-wideband systems. The proposed detection scheme relies on both the introduction of the cyclic prefix at the transmitter and the use of a frequency domain detection approach at the receiver. Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) multiuser detectors have been investigated and compared with the classical RAKE, considering a scenario where several mobile terminals communicate with a base station in an indoor environment characterized by severe multipath propagation. The results show that the MMSE receiver achieves the best performance, irrespective of the number of active terminals, both in the up-link and in the downlink communications. Hence, the proposed detectors appear to be well suited for future indoor wireless applications.

In this paper, we investigate the performance of a generic filterbank transceiver in the presence of a carrier frequency offset. We propose a theoretical model to compute the error probability in the case of a generic frequency-selective channel. Moreover, the proposed method is also extended to deal with nonuniform, i.e., wavelet, transceivers. The accuracy of the model is evaluated by means of computer simulations, using several types of filterbank transceivers. Systems based on cyclic prefix (CP) and zero padding (ZP) are considered to avoid interblock interference (IBI) in frequency-selective channels. The analytical results obtained with the proposed method allow us to quickly compare different systems, characterized by a different filterbank selectivity, as well as by different methods to combat IBI (ZP or CP).

We investigate the performance of filterbank-based MC-CDMA systems in the presence of a carrier frequency offset. We propose a semi-analytical method to compute the error probability when both generic transmit filters and arbitrary linear receivers are used, considering a multipath fading channel in the downlink. In particular, systems based on cyclic prefix (CP) and zero padding (ZP) are considered to avoid inter-block interference (IBI). The accuracy of the method is evaluated by means of computer simulations, considering several types of filterbank-based MC-CDMA systems, and it is shown to be very accurate. The analytical results obtained with the proposed method allow us to compare, in a fast way, different systems characterized by different methods to combat IBI (ZP or CP), as well as by different receiver structures.

We investigate the performance of a transceiver based on wavelet packet modulation in presence of a carrier frequency offset. We propose a theoretical model to compute the error probability in the case of an uniform frequency subdivision, considering both the AWGN channel and an arbitrary frequency-selective channel. Then, we extend the proposed method to deal with nonuniform, i.e. wavelet, transceivers. The accuracy of the model is evaluated by means of computer simulations.

In this paper we propose an innovative communication scheme for impulse radio ultra-wideband systems which is based on both the introduction of the cyclic prefix at the transmitter and the use of a frequency domain detector at the receiver. Two different approaches based on the zero forcing (ZF) and the minimum mean square error (MMSE) criteria have been investigated. We have compared our frequency domain approach with the classical RAKE, considering an illustrative scenario where a base station transmits to several mobile terminals in an indoor environment characterized by severe multipath propagation. The results show that the receiver based on the MMSE criterion achieves the best performance with respect to the other schemes, irrespective of the number of active terminals. Hence, the proposed approach is well suited in indoor wireless environment where multipath propagation tends to increase the effects of both the inter-path and the inter-user interference.

We investigate the performance of filterbank transceivers in the presence of a dispersive time-variant channel. It is well-known that filterbank transceivers can be adapted to the channel transfer function to yield intersymbol interference (ISI) cancellation. When the channel is time-variant, several problems arise, since the transceiver should be changed whenever the channel evolves. We allow both the transmitter and the receiver to change and satisfy the ISI-free condition, under the assumption of a zero padded block transmission. In this case, the transmitter-receiver pair can be computed by using a singular value decomposition (SVD) of the channel matrix. A fast receiver adaptation based on SVD tracking is presented. Simulation results show that minimum performance loss can be achieved for our reduced complexity receiver.