With the growing integration of biometric recognition systems into high-security and large-scale deployment scenarios, it is becoming increasingly important to ensure their robustness under realistic operational constraints. This implies designing solutions able to withstand potential adversarial threats that could affect their integrity and accountability, and also taking into account requirements of real-world operating systems such as limited availability of bandwidth and memory for data transmission and storage. Hence, the proposed study deals with presentation attack detection (PAD) for iris recognition, evaluating the effectiveness of Transformer-based frameworks at detecting spoofing attacks relying on fabricated or artificial biometric evidences. More specifically, we focus on the effects of image compression on the quality of iris images and on the resulting PAD performance, considering both traditional techniques such as JPEG as well as next-generation learning-based image codecs such as JPEG AI. We then examine the feasibility of mitigating compression-induced performance degradation by fine-tuning the adopted models on compressed images, achieving improvements in terms of half total error rate between 5% and 10% for images compressed at the worst JPEG and JPEG AI qualities. We also evaluate the generalizability of the developed solutions by testing them on learning-based codecs not considered during training, to check whether similar PAD-relevant artifacts are introduced by different compressions. Furthermore, we investigate the redundancy within the embeddings generated by the employed detectors, and demonstrated it is possible to significantly compress them while preserving the achievable PAD performance. Overall, the study provides a systematic analysis of iris PAD under compression constraints, offering insights into model adaptation, cross-codec robustness, and representation efficiency in scenarios where visual data coding plays a central role.

Template protection is an issue of paramount importance for the design of secure and privacy-compliant biometric recognition systems. Template unlinkability, together with template irreversibility, is an essential requirement to properly guarantee template protection. In fact, it ensures that templates generated from the same trait, but used in different applications, cannot be linked to the same identity. This paper deals with the design of a system satisfying the unlinkability requirement. The robustness of the proposed solution is evaluated by exploiting methods stemming from the theory of stochastic optimization, as well as by using quantitative measures specifically proposed to characterize the unlinkability of biometric protection schemes. A case study using finger-vein biometrics is considered to test the proposed cryptosystem on non-ideal data. The proposed scheme guarantees 128 bits of security with acceptable false recognition rates in real-life conditions. Moreover, we provide guidelines to determine the parameters of the transformations to be applied to real biometric traits so as to ensure proper recognition, security, and unlinkability performance.

Swapping faces with deep learning technology to generate realistic fake videos/images (a.k.a, deepfakes) has drawn great public con-cerns recently. Numerous approaches have been proposed to iden-tify fake contents; however, less work has been dedicated to pro-tecting the source faces in an active way. In this paper, we stand for a legitimate faceswap service provider and present an approach called Source-ID- Tracker (SIDT), which aims to protect the identity of source faces in deepfakes from malicious uses. As a plug-in, the encoder of SIDT implicitly embeds a source face image into a deep-fake image while ensuring the resultant encoded image is visually indistinguishable from the deepfake image. After sharing through social media, the embedded source face and its identity can still be recovered with a decoder. Experimental results show that the pro-posed model achieves a promising performance, in terms of reconstruction quality and attribution inference accuracy, in revealing the hidden source face.

In this letter, we propose a vein-based biometric verification system relying on deep learning. A novel approach consisting of a convolutional neural network (CNN), trained in a supervised manner, cascaded with an auto-encoder, trained in an unsupervised way, is here exploited. In more detail, a novel densely-connected convolutional autoencoder is here used on top of backbone CNNs. This architecture aims at increasing the discriminative capability of the features generated from hand vein patterns. Experimental tests on finger, palm, and dorsal veins show that the proposed approach leads to an improvement of the recognition rates with respect to the use of the sole CNNs for feature extraction. The achieved performance are superior to the current state of the art in vein biometric verification.

Finger-vein-based biometric recognition technology has recently attracted the attention of both academia and industry because of its robustness against presentation attacks and the convenience of the acquisition process. As a matter of fact, some contactless vein-based recognition systems have already been deployed and commercialized. However, they require the users to keep their hands still over the acquisition device for a few seconds to perform recognition. In this study, we release this constraint and allow users to have their finger vein patterns acquired on-the-fly. To accomplish this goal, we introduce an ad-hoc acquisition architecture capable of capturing the finger vein structure using an array of low-cost cameras, and we propose a recognition framework based on the use of convolutional and recurrent neural networks. To test the proposed approach we acquire a finger vein image dataset, in video format at four different exposure times, from 100 subjects. The obtained experimental results show that, even in a very challenging scenario, the proposed system guarantees high performance levels, up to 99.13% recognition accuracy over the collected dataset.

The use of human finger-vein traits for the purpose of automatic user recognition has gained a lot of attention in recent years. Current state-of-the-art techniques can provide relatively good performance, yet they are strongly dependent upon the quality of the analyzed finger-vein images. In this paper, we propose a convolutional-neural-network-based finger-vein identification system and investigate the capabilities of the designed network over four publicly available databases. The main purpose of this paper is to propose a deep-learning method for finger-vein identification, which is able to achieve stable and highly accurate performance when dealing with finger-vein images of different quality. The reported extensive set of experiments show that the accuracy achievable with the proposed approach can go beyond 95% correct identification rate for all the four considered publicly available databases.

This paper deals with electroencephalography (EEG)-based biometric identification, using a motor imagery task, specifically performing imaginary arms and legs movements. Deep learning methods such as convolutional neural network (CNN) is used for automatic discriminative feature extraction and person identification. An extensive set of experimental tests, performed on a large database comprising EEG data collected from 40 subjects over two different sessions taken at a week distance, shows the existence of repeatable discriminative characteristics in individuals' brain signals.

It is with a sense of honor, privilege, and excitement that, with this January issue, I start my term as Editor-in-Chief of the IEEE Transactions on Information Forensics and Security (T-IFS). More than a decade has passed since the first issue of T-IFS has been published as the result of a long term editorial project of the Signal Processing Society to satisfy the always increasing demand to cover emerging topics that have been later embraced by the wording “information forensics and security.” In this decade, research has naturally evolved and some topics have become less attractive while others have become prominent. T-IFS has been able to adapt to these changes, as a container whose boundaries are not crystallized and immutable but that can dynamically embrace the new challenges posed by our scientific and industrial world. This is reflected by the evolution of the T-IFS’ EDICS across the years.

Brain signals have recently attracted the attention of the scientific community as potential biometric identifiers. In more detail, there is a growing interest in evaluating the feasibility of using electroencephalography (EEG) recordings to perform automatic people recognition. In this scenario, the study of the longitudinal behavior of EEG signals, i.e., their permanence across time, is of paramount importance. This paper is the first extensive attempt, in terms of employed elicitation protocols, number of involved subjects, number of acquisition sessions, and covered time span, to evaluate the influence of aging effects on the discriminative capabilities of EEG signals over long-term periods. Specifically, we here report and discuss the results obtained from experimental tests conducted on a database comprising 45 subjects, whose EEG signals have been collected during five to six distinct sessions spanning a total period of three years, using four different elicitation protocols. The longitudinal behavior of EEG discriminative traits is evaluated by means of a statistical-and performance-related analysis, using different EEG features and hidden Markov models as classifiers. A characterization of each considered EEG channel in terms of uniqueness and permanence properties is also performed, with the purpose of ranking their relevance for biometric purposes, thus giving hints to contain their number in practical applications. Moreover, we design some possible countermeasures to mitigate aging effects on recognition performance and evaluate their effectiveness, thus paving the road for the future deployment of real-life cognitive recognition systems relying on brain-based biometric traits.

In this paper, we present a novel biometric cryptosystem obtaining perfect security, that is not leaking any information about the employed secret key from the knowledge of the stored helper data. While similar purposes have already been sought in the literature, the approaches proposed so far have been evaluated in terms of recognition performance under the unrealistic assumption of ideal statistical distributions for the considered biometric data. Conversely, in this paper, we investigate the applicability of the proposed framework to practical scenarios while managing a trade-off between privacy and recognition performance. This goal has been achieved by introducing a class of transformation functions enforcing zero-leakage secrecy, by designing an adaptive strategy for embedding the secret key bits into the selected features, and by developing a system parameters optimization strategy with respect to security, recognition performance, and privacy. Experimental tests conducted on real fingerprint data prove the effectiveness of the proposed scheme.

A blind multi-bit watermarking method, specifically designed for high dynamic range (HDR) images, is introduced in thiswork. The presented embedding strategy takes into account several properties of the human visual system in order to guar-antee proper imperceptibility of the embedded watermarks, as well as robustness against tone-mapping operators (TMOs)employed to derive low dynamic range (LDR) representations of the marked HDR content. Specifically, binary marks areembedded into the discrete wavelet transform (DWT) of the just-noticeable difference domain. Additionally, embeddinglocations are preferably selected in correspondence of the detail parts of an HDR image, determined by exploiting the prop-erties of bilateral filtering. Furthermore, the watermark strength adopted in each DWT subband is modulated depending ona contrast sensitivity function, depending of the subband scale and orientation, to improve the visual quality of the water-marked image. In order to verify the effectiveness of the proposed approach, in terms of both mark imperceptibility androbustness, several experimental tests are carried out on a database comprising 15 HDR images. Specifically, robustness isevaluated by blindly extracting the embedded messages from both HDR marked images and from their LDR counterparts,thus investigating the effects of TMOs on message recovery.

In this letter, we investigate the permanence issue of electroencephalographic (EEG) signals, elicited by visual stimuli, for biometric recognition purposes. Specifically, we evaluate the discriminative capabilities of generic visually-evoked potentials (VEPs) and of visual event-related potentials (ERPs) associated to specific cognitive tasks. Furthermore, we analyze the permanence issue of the considered EEG traits by verifying the stability across time of the achievable recognition rates. Experimental tests performed on a longitudinal database, comprising EEG data taken from 50 subjects during 3 different sessions, give evidence of the presence of repeatable discriminative characteristics in the individuals' EEG activity.

Brain signals have been investigated for more than a century in the medical field. However, despite the broad interest in clinical applications, their use as a biometric identifier has been only recently considered by the scientific community. In this paper, we focus on the permanence across time of brain signals, specifically of electroencephalographic (EEG) signals, issue of paramount importance for the deployment of brain-based biometric recognition systems in real life, not yet fully addressed. In particular, we speculate about the stability of EEG features by analyzing the recognition performance that can be achieved when comparing EEG signals acquired during different sessions. We carry out an extensive set of experimental tests, performed on several EEG-based biometric systems over a large database, comprising three recordings taken from 50 healthy subjects in resting state conditions, acquired in a time span of approximately one month and a half. The results confirm that a significant level of permanence can be guaranteed.

Biometric recognition systems, despite the advantages provided with respect to traditional authentication methods, have some peculiar weaknesses which may allow an attacker being falsely recognized or accessing users' personal data. Among such vulnerabilities, in this paper, we speculate on the hill-climbing attack, i.e., the possibility for an attacker to exploit the scores produced by the matcher with the goal of generating synthetic biometric data, which could allow a false acceptance. More in detail, we focus on multibiometrics systems and investigate about the robustness of different system architectures, both parallel and serial fusion schemes, against the hill-climbing attack. Nonuniform quantization is also evaluated as a possible countermeasure for limiting the effectiveness of the considered attacks in terms of recognition success rate and average number of required attempts without affecting the recognition performance.

Template protection is an issue of paramount importance in the design of biometric recognition systems. In this paper we present a biometric cryptosystem applied to iris biometrics, where template security is guaranteed by means of a framework inspired by the digital modulation paradigm. Specifically, the properties of modulation constellations and turbo codes with soft-decoding are exploited to design a system with high performance in terms of both verification rates and security, even while dealing with a biometrics characterized by a high intra-class variability such as the iris. The effectiveness of the proposed approach is evaluated by performing tests on the Interval subset of the CASIA-IrisV4 database.

Brain signals have been investigated within the medical field for more than a century to study brain diseases like epilepsy, spinal cord injuries, Alzheimer's, Parkinson's, schizophrenia, and stroke among others. They are also used in both brain computer and brain machine interface systems with assistance, rehabilitative, and entertainment applications. Despite the broad interest in clinical applications, the use of brain signals has been only recently investigated by the scientific community as a biometric characteristic to be used in automatic people recognition systems. However, brain signals present some peculiarities, not shared by the most commonly used biometrics, such as face, iris, and fingerprints, with reference to privacy compliance, robustness against spoofing attacks, possibility to perform continuous identification, intrinsic liveness detection, and universality. These peculiarities make the use of brain signals appealing. On the other hand, there are many challenges which need to be properly addressed. The understanding of the level of uniqueness and permanence of brain responses, the design of elicitation protocols, and the invasiveness of the acquisition process are only few of the challenges which need to be tackled. In this paper, we further speculate on those issues, which represent an obstacle toward the deployment of biometric systems based on the analysis of brain activity in real life applications and intend to provide a critical and comprehensive review of state-of-the-art methods for electroencephalogram-based automatic user recognition, also reporting neurophysiological evidences related to the performed claims.

Data security and privacy are crucial issues to be addressed for assuring a successful deployment of biometrics-based recognition systems in real life applications. In this paper, a template protection scheme exploiting the properties of universal background models, eigen-user spaces, and the fuzzy commitment cryptographic protocol is presented. A detailed discussion on the security and information leakage of the proposed template protection system is given. The effectiveness of the proposed approach is investigated with application to online signature recognition. The given experimental results, evaluated on the public MCYT signature database, show that the proposed system can guarantee competitive recognition accuracy while providing protection to the employed biometric data.

A new diadic family of eigenfunctions of the 2-D Fourier transform has been discovered. Specifically, new wavelets are derived by steering the elongated Hermite-Gauss filters with respect to rotations, thus obtaining a natural generalization of the Laguerre-Gauss harmonics. Interestingly, these functions are also proportional to their 2-D Fourier transform. Their analytical expression is provided in a compact and treatable form, by means of a new ad hoc matrix notation in which the cases of even and odd orders of the Hermite polynomials are unified. Moreover, these functions can be efficiently implemented by means of a recursive formula that is derived in this paper. The proposed filters are applied to the problem of gradient estimation to improve the theoretical Canny tradeoff of position accuracy versus noise rejection that occurs in edge detection. Experimental results show considerable improvements in using the new wavelets over both isotropic Gaussian derivatives and other elongated steerable filters more recently introduced. Finally, being the proposed wavelets a set of Fourier eigenfunctions, they can be of interest in other fields of science, such as optics and quantum mechanics.

We provide a closed form, both in the spatial and in the frequency domain, of a family of wavelets which arise from steering elongated Hermite-Gauss filters. These wavelets have interesting mathematical properties, as they form new dyadic families of eigenfunctions of the 2D Fourier transform, and generalize the well known Laguerre-Gauss harmonics. A special notation introduced here greatly simplifies our proof and unifies the cases of even and odd orders. Applying these wavelets to edge detection increases the performance of about 12.5% with respect to standard methods, in terms of the Pratt's figure of merit, both for noisy and noise-free input images.

In this paper, we perform approximate steering of the elongated 2D Hermite-Gauss functions with respect to rotations and provide a compact analytical expressions for the related basis functions. A special notation introduced here considerably simplifies the derivation and unifies the cases of even and odd indices. The proposed filters are applied to edge detection. Quantitative analysis shows a performance increase of about 12.5% in terms of the Pratt's figure of merit with respect to the well-established Gaussian gradient proposed by Canny.

In this paper we propose a biometric cryptosystem able to provide security and renewability to a function based on- line signature representation. A novel reliable signature traits selection procedure, along with a signature binarization algorithm, are introduced. Experimental results, evaluated on the public MCYT signature database, show that the proposed protected on-line signature recognition system guarantees recognition rates comparable with those of unprotected approaches, and outperforms already proposed protection schemes for signature biometrics.

Recent years have seen the rapid spread of biometric technologies for automatic people recognition. However, security and privacy issues still represent the main obstacles for the deployment of biometric-based authentication systems. In this paper, we propose an approach, which we refer to as BioConvolving, that is able to guarantee security and renewability to biometric templates. Specifically, we introduce a set of noninvertible transformations, which can be applied to any biometrics whose template can be represented by a set of sequences, in order to generate multiple transformed versions of the template. Once the transformation is performed, retrieving the original data from the transformed template is computationally as hard as random guessing. As a proof of concept, the proposed approach is applied to an on-line signature recognition system, where a hidden Markov model-based matching strategy is employed. The performance of a protected on-line signature recognition system employing the proposed BioConvolving approach is evaluated, both in terms of authentication rates and renewability capacity, using the MCYT signature database. The reported extensive set of experiments shows that protected and renewable biometric templates can be properly generated and used for recognition, at the expense of a slight degradation in authentication performance.

3DTV has been widely studied these last years from a technical point of view but the related perceived quality evaluations do not follow this hype. This paper firstly reviews quality assessment issues for 3DTV. Compared to 2D quality measure, depth information adds several new problems considering quality assessment. Nevertheless, efforts made for 2D content quality estimation can be used for an extension to 3D. In a second part, this paper proposes an adaptation of 2D metrics to 3D in the context of coding artifacts and stereoscopic images. Distortion on disparity is introduced to improve conventional 2D metrics. Performances have been evaluated using subjective tests.

In this paper we propose a new joint encryption and lossless compression technique designed for large images. The proposed technique takes advantage of the Mojette transform properties, and can easily be included in a distributed storage architecture. The basic crypto-compression scheme presented is based on a cascade of Radon projection which enables fast encryption of a large amount of digital data. Standard encryption techniques, such as AES, DES, 3DES, or IDEA can be applied to encrypt very small percentages of high resolution images. As the proposed scheme uses standard encryption, and only transmits uncorrelated data along with the encrypted part, this technique takes benefit of the security related to the chosen encryption standard, here, we assess its performances in terms of processing time and compression ratio.

In this paper we propose a new method for natural video textures synthesis, using a three-dimensional (3-D) extended self-similar (ESS) fractal model. The autocorrelation functions (ACFs) of original texture increments are estimated, and a synthetic 3-D-ESS process whose increments have the same ACFs of the corresponding given ones is generated using a 3-D incremental Fourier synthesis algorithm. Experimental results for the analysis and synthesis of natural video textures are provided

Two important visual properties of paintings and painting-like images are the absence of texture details and the increased sharpness of edges as compared to photographic images. Painting-like artistic effects can be achieved from photographic images by filters that smooth out texture details, while preserving or enhancing edges and corners. However, not all edge preserving smoothers are suitable for this purpose. We present a simple nonlinear local operator that generalizes both the well known Kuwahara filter and the more general class of filters known in the literature as "criterion and value filter structure." This class of operators suffers from intrinsic theoretical limitations which give rise to a dramatic instability in presence of noise, especially on shadowed areas. Such limitations are discussed in the paper and overcome by the proposed operator. A large variety of experimental results shows that the output of the proposed operator is visually similar to a painting. Comparisons with existing techniques on a large set of natural images highlight conditions on which traditional edge preserving smoothers fail, whereas our approach produces good results. In particular, unlike many other well established approaches, the proposed operator is robust to degradations of the input image such as blurring and noise contamination.

In natural images, luminance changes occur both on object contours and on textures. Often, the latter are stronger than the former, thus standard edge detectors fail in isolating object contours from texture. To overcome this problem, we propose a multiresolution contour detector motivated by biological principles. At each scale, texture is suppressed by using a bipolar surround inhibition process. The binary contour map is obtained by a contour selection criterion that is more effective than the classical hysteresis thresholding. Robustness to noise is achieved by Bayesian gradient estimation.

In this paper, we present a texture classification procedure that makes use of a blind deconvolution approach. Specifically, the texture is modeled as the output of a linear system driven by a binary excitation. We show that features computed from one-dimensional slices extracted from the two-dimensional autocorrelation function (ACF) of the binary excitation allows representing the texture for rotation-invariant classification purposes. The two-dimensional classification problem is thus reconduced to a more simple one-dimensional one, which leads to a significant reduction of the classification procedure computational complexity.

A novel video watermarking system operating in the three-dimensional discrete wavelet transform (3D DWT) is here presented. Specifically the video sequence is partitioned into spatio-temporal units and the single shots are projected onto the 3D DWT domain. This paper focuses on the definition of a perceptual mask, derived from the 3D wavelet coefficients, which allows to trade off between the mark robustness and its imperceptibility. The proposed mask takes into account the spatio-temporal frequency content, the variance, and the luminance of the 3D subbands where the mark is embedded. The embedding is additively performed in the 3D DWT domain by weighing the mark through the defined mask. Finally, the inverse 3D DWT (IDWT) is performed thus obtaining the marked video shot. The imperceptibility of the proposed technique along with its robustness has been observed through experimental results.

Video watermarking methods operating in the three-dimensional discrete wavelet transform (3D-DWT) domain using quantization-based embedding techniques are here proposed. Specifically, the video sequence is partitioned into spatio-temporal units of fixed length. Then the video shots undergo a three level 3D-DWT. Two embedding methods are here considered: quantization index modulation and rational dither modulation. Finally the inverse 3D-DWT is performed thus obtained the marked video shot. The effectiveness of the proposed methods has been verified experimentally. They guarantee a high mark imperceptibility as well as robustness to attacks such as MPEG2 compression, MPEG4 compression, collusion, transcoding, and frame dropping. Gain attack is also considered

In this paper, a new methodology for embedding data in a video sequence is presented. To guarantee the imperceptibility of the embedded data, we propose a method to select frame regions that are considered perceptually non relevant. For each frame a salience analysis is performed based on features that are relevant to the human vision system. In particular, the local contrast, the color and the areas of motion have been considered. By weighting all these feature at once, an importance map is built to drive the sequent embedding procedure. Subjective experiment results show that the artifacts caused by this localized embedding procedure are considered less annoying than if the embedding is performed on the whole frame

In this letter, we propose a multiplicative watermarking method operating in the ridgelet domain. We employ the directional sensitivity and the anisotropy of the ridgelet transform (RT) in order to obtain a sparse image representation, where the most significant coefficients represent the most energetic direction of an image with straight edges. Therefore, given a natural image, the associated edge image is obtained by means of a filter bank designed using the circular harmonic functions, then the edge image is partitioned into small blocks in order to deal with straight edges. Finally, the RT is performed for each block, the most relevant coefficients are selected, and eventually the watermark is embedded. Robustness and transparency are proven by experimental results.

In this paper, a model based texture classification procedure is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We show that features extracted from the ACF of the binary excitation suffice to represent the texture for classification purposes. Specifically, we employ a moment invariants based technique to classify the ACF. The resulting proposed classification procedure is thus inherently rotation invariant. Moreover, it is robust with respect to additive noise. Experimental results show that this approach allows obtaining high correct rotation-invariant classification rates while containing the size of the feature space.

This paper proposes an approach for the combined image authentication and compression of color images by making use of a digital watermarking and data hiding framework. The digital watermark is comprised of two components: a soft-authenticator watermark for authentication and tamper assessment of the given image, and a chrominance watermark employed to improve the efficiency of compression. The multipurpose watermark is designed by exploiting the orthogonality of various domains used for authentication, color decomposition and watermark insertion. The approach is implemented as a DCT-DWT dual domain algorithm and is applied for the protection and compression of cultural heritage imagery. Analysis is provided to characterize the behavior of the scheme under ideal conditions. Simulations and comparisons of the proposed approach with state-of-the-art existing work demonstrate the potential of the overall scheme.

This work addresses the multichannel Bussgang restoration algorithm for blind image deconvolution problems. The case of spatial correlated nonGaussian images is considered, and the structure of nonlinear minimum mean square error estimators, to be used in the Bussgang deconvolution algorithm, is discussed. In particular, it is conjectured that, for nonGaussian, spatially correlated random fields, a better nonlinear estimator is obtained in a suitable wavelet transformed domain. The wavelet transform here considered is the so-called circular harmonic wavelet (CHW), because of its characteristic property of capturing specific image features, such as edges, lines, forks, crosses, and so on. Experimental results pertaining to restoration of motion blurred natural images are also reported.

This work extends the Bussgang blind equalization algorithm to the multichannel case with application to image deconvolution problems. We address the restoration of images with poor spatial correlation as well as strongly correlated (natural) images. The spatial nonlinearity employed in the final estimation step of the Bussgang algorithm is developed according to the minimum mean square error criterion in the case of spatially uncorrelated images. For spatially correlated images, the nonlinearity design is rather conducted using a particular wavelet decomposition that, detecting lines, edges, and higher order structures, carries out a task analogous to those of the (preattentive) stage of the human visual system. Experimental results pertaining to restoration of motion blurred text images, out-of-focus spiky images, and blurred natural images are reported.

Multimedia data hiding by digital watermarking is usually employed for copyright protection purposes. In this contribution, a new application of watermarking is presented. Specifically, watermarking is here employed as a technique for testing the quality of service in multimedia mobile communications. A fragile known watermark is embedded in a MPEG-like host data video transport stream using a spread-spectrum technique to avoid visual interference. Like a tracing signal, a (known) tracing watermark tracks the (unknown) information stream that follows the same communication link. The detection of the tracing watermark allows dynamically evaluating the effective quality of the provided video services, depending on the whole physical layer (including the employed image co/decoder). The performed method is based on the mean-square-error between estimated and actual watermarks. The devised technique has been usefully applied to typical scenarios of mobile wireless multimedia communication systems, in presence of multipath channel and interfering users.

In this paper a model based texture classification procedure robust to sample rotation is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We show that features extracted from the ACF of the binary excitation suffice to represent the texture for classification purposes. Specifically, we employ a classical moment invariants based technique to classify the ACF and the resulting classification procedure is thus inherently rotation invariant. Experimental results show that this approach allows obtaining high correct rotation-invariant classification rates while reducing the size of the feature space and the computational burden.

Third generation cellular systems (IMT2000/UMTS) are expected to provide a large number of innovative services with negotiated end-to-end quality of service (QoS). An object-based system for quality of service assessment is presented to evaluate blindly the quality of a multimedia communication link by means of an unconventional use of semi-fragile digital watermarking. Specifically, a known watermark is hidden into each single object of a MPEG-4 video using a spread spectrum technique. In this fashion, the object and the embedded watermark follow the same communication link. In fact, the degradations endured by the (known) watermark allows the degradations suffered, at the receiver's side, by the original (unknown) object to be inferred. The quality of service evaluation is based on the calculation of the mean square error between the estimated and the actual watermarks. Experiments show the effectiveness of the proposed method.

In this paper, an unsupervised model-based texture reproduction technique is described. In accordance with the Julesz's (1962) conjecture, the statistical properties of the prototype up to the second order are copied in order to generate a synthetic texture perceptually indistinguishable from the given sample. However, this task is accomplished using a hybrid approach which operates partially in the spatial domain and partially in a multiresolution domain. The latter employed is the circular harmonic function (CHF) domain since it has been proven to be well suited for mimicking the behavior of the human visual system (HVS). This approach allows, for a wide range of textures typologies, obtaining synthetic textures that better match the prototype with respect to the ones obtained using techniques based on the Julesz's conjecture operating only in the spatial domain, and to dramatically reduce the computational complexity of similar methods operating only in the multiresolution domain.

Video databases are very demanding systems as far as mass storage requirements and computational resources necessary to perform common database operations, such as browsing and retrieval, are required. These operations can be simplified both in terms of the computational complexity and of processing time by performing them on an ensemble of frames, called key frames, representative of the content units (shots) in which a video can be segmented. An adaptive key frames extraction method based on a wavelet based multiresolution analysis in a perceptually uniform color space is presented. Experimental results that show the effectiveness of the proposed technique to select key frames summarizing the video's content, are finally provided.

The problem of image texture stochastic modeling by means of the 2D Wold decomposition is addressed. Specifically, the 2D Wold decomposition additively separates a texture into a deterministic and an indeterministic component. Since these components are shown to be statistically orthogonal, for textures having a distribution that significantly deviates from Gaussianity, it is possible, in principle, to establish some link between the two components. The aim of this contribution is to show how the indeterministic component can be predicted from the deterministic one by means of suitable nonlinear schemes. The hierarchical structure of the 2D Wold decomposition is thus generalized to deal with the non-Gaussian behavior of most of the natural textures of interest.

In natural scenes, still images as well as sequences, backgrounds, and objects' surfaces usually have a textural structure. Therefore, in order to efficiently code images it is crucial to investigate the texture compression problem. In this paper, a perceptually lossless, synthesis-by-analysis texture coding method is presented. The proposed approach is model based; the parameters of the model consist of a binary excitation signal and the parsimonious representation of the reconstruction filter. The estimated parameters, which allow to one synthesize, at the decoder site, a texture that is perceptually indistinguishable from the original one, are then compressed using a lossless strategy, which is based on a fast binary wavelet transformation specifically tailored to binary images. The proposed method leads to very good perceptual results superior to those of existing techniques.

An unsupervised color texture synthesis-by analysis method is described. The texture is reproduced to appear perceptually similar to a given prototype by copying its statistical properties up to the second order. The synthesized texture is obtained at the output of a single-input three-output nonlinear system driven by a realization of a white Gaussian random field. Significant complexity reduction is gained by exploiting the rank deficiency of the cross power spectral density matrix of the color texture samples.

In this paper a video coder based on a spatiotemporal multiresolution pyramid generated by a 3D separable wavelet transform (WT) is presented. The aim of the proposed coding scheme is to provide an effective scalability of the user profile by organizing the full resolution encoded bit stream into substreams, one for each temporal layer of the pyramid, which are then partitioned into substreams at increasing spatial resolution. This allows to simultaneously distribute, at different time and space resolutions, the same video to multiple users accessing the videoconference services through channels of different capacity, without multiple encoding.

In this paper, we propose a noise filtering scheme, which is based on a multichannel homomorphic transformation, for color photographic images corrupted by signal-dependent film grain noise. The proposed method performs the estimation of the noise parameter using higher-order statistics (skewness or kurtosis) of the corrupted image and filtered image statistics. This parameter estimation technique can be used to generate color film grain noise that has applications in motion picture productions. After a theoretical description of the method employed, experimental results are provided.

An innovative unsupervised color texture, model based, synthesis method is presented. It aims to reproduce a color texture perceptually similar to a given prototype. To achieve this task, we reconstruct the texture on a stochastic base, matching the prototype's statistics up to the second order to the synthesized texture ones. The model employed allows to replace the multiple input multiple output filters, which have to be used in the general case, with more simple structures, leading to a dramatical computational complexity reduction without affecting the results quality.