Showing papers by "Thierry Pun published in 2006"

Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals

Abstract: The arousal dimension of human emotions is assessed from two different physiological sources: peripheral signals and electroencephalographic (EEG) signals from the brain. A complete acquisition protocol is presented to build a physiological emotional database for real participants. Arousal assessment is then formulated as a classification problem, with classes corresponding to 2 or 3 degrees of arousal. The performance of 2 classifiers has been evaluated, on peripheral signals, on EEG's, and on both. Results confirm the possibility of using EEG's to assess the arousal component of emotion, and the interest of multimodal fusion between EEG's and peripheral physiological signals.

Multilevel 2-D Bar Codes: Toward High-Capacity Storage Modules for Multimedia Security and Management

Abstract: In this paper, we deal with the design of high-rate multilevel 2-D bar codes for the print-and-scan channel. First, we introduce a framework for evaluating the performance limits of these codes by studying an intersymbol-interference (ISI)-free, synchronous, and noiseless print-and-scan channel, where the input and output alphabets are finite and the printer device uses halftoning to simulate multiple gray levels. Second, we present a new model for the print-and-scan channel specifically adapted to the problem of communications via multilevel 2-D bar codes. This model, inspired by our experimental work, assumes perfect synchronization and absence of ISI, but independence between the channel input and the noise is not assumed. We adapt the theory of multilevel coding with multistage decoding (MLC/MSD) to the print-and-scan channel. Finally, we present experimental results confirming the utility of our channel model, and showing that multilevel 2-D bar codes using MLC/MSD can reliably achieve the high-capacity storage requirements of many multimedia security and management applications

Brand protection and product autentication using portable devices

Abstract: The present invention is a method and apparatus for protection of products and packaging against counterfeiting using dedicated authentication protocol coupled with portable devices It is based on the product identification information, ie, PIN, generated by the product manufacturer, stored in the product database and added to product or packaging in an open and/or a hidden form The open part is directly available to the consumer before buying, opening or consuming the product or package or damaging its integrity while the hidden part is only revealed after these operations The hidden information can also be disappearing after a predefined interval of time or number of trials or usages Both parts are communicated to the authentication server in a predefined order to verify the product or package authenticity The presence, absence, or multiple requests for the same product PIN, confirm or reject product authenticity or detect attempt at attacking the system or at using counterfeited products

Text data-hiding for digital and printed documents: theoretical and practical considerations

Abstract: In this paper, we propose a new theoretical framework for the data-hiding problem of digital and printed text documents. We explain how this problem can be seen as an instance of the well-known Gel'fand-Pinsker problem. The main idea for this interpretation is to consider a text character as a data structure consisting of multiple quantifiable features such as shape, position, orientation, size, color, etc. We also introduce color quantization, a new semi-fragile text data-hiding method that is fully automatable, has high information embedding rate, and can be applied to both digital and printed text documents. The main idea of this method is to quantize the color or luminance intensity of each character in such a manner that the human visual system is not able to distinguish between the original and quantized characters, but it can be easily performed by a specialized reader machine. We also describe halftone quantization, a related method that applies mainly to printed text documents. Since these methods may not be completely robust to printing and scanning, an outer coding layer is proposed to solve this issue. Finally, we describe a practical implementation of the color quantization method and present experimental results for comparison with other existing methods.

Brain-computer interaction research at the computer vision and multimedia laboratory, University of Geneva

Abstract: This paper describes the work being conducted in the domain of brain-computer interaction (BCI) at the Multimodal Interaction Group, Computer Vision and Multimedia Laboratory, University of Geneva, Geneva, Switzerland. The application focus of this work is on multimodal interaction rather than on rehabilitation, that is how to augment classical interaction by means of physiological measurements. Three main research topics are addressed. The first one concerns the more general problem of brain source activity recognition from EEGs. In contrast with classical deterministic approaches, we studied iterative robust stochastic based reconstruction procedures modeling source and noise statistics, to overcome known limitations of current techniques. We also developed procedures for optimal electroencephalogram (EEG) sensor system design in terms of placement and number of electrodes. The second topic is the study of BCI protocols and performance from an information-theoretic point of view. Various information rate measurements have been compared for assessing BCI abilities. The third research topic concerns the use of EEG and other physiological signals for assessing a user's emotional status.

The Gaussian Transform of Distributions: Definition, Computation and Application

Abstract: This paper introduces the general-purpose Gaussian transform of distributions, which aims at representing a generic symmetric distribution as an infinite mixture of Gaussian distributions. We start by the mathematical formulation of the problem and continue with the investigation of the conditions of existence of such a transform. Our analysis leads to the derivation of analytical and numerical tools for the computation of the Gaussian transform, mainly based on the Laplace and Fourier transforms, as well as of the afferent properties set (e.g., the transform of sums of independent variables). The Gaussian transform of distributions is then analytically derived for the Gaussian and Laplacian distributions, and obtained numerically for the generalized Gaussian and the generalized Cauchy distribution families. In order to illustrate the usage of the proposed transform we further show how an infinite mixture of Gaussians model can be used to estimate/denoise non-Gaussian data with linear estimators based on the Wiener filter. The decomposition of the data into Gaussian components is straightforwardly computed with the Gaussian transform, previously derived. The estimation is then based on a two-step procedure: the first step consists of variance estimation, and the second step consists of data estimation through Wiener filtering. To this purpose, we propose new generic variance estimators based on the infinite mixture of Gaussians prior. It is shown that the proposed estimators compare favorably in terms of distortion with the shrinkage denoising technique and that the distortion lower bound under this framework is lower than the classical minimum mean-square error bound

Information-theoretic temporal segmentation of video and applications: multiscale keyframes selection and shot boundaries detection

Abstract: The first step in the analysis of video content is the partitioning of a long video sequence into short homogeneous temporal segments. The homogeneity property ensures that the segments are taken by a single camera and represent a continuous action in time and space. These segments can then be used as atomic temporal components for higher level analysis like browsing, classification, indexing and retrieval. The novelty of our approach is to use color information to partition the video into segments dynamically homogeneous using a criterion inspired by compact coding theory. We perform an information-based segmentation using a Minimum Message Length (MML) criterion and minimization by a Dynamic Programming Algorithm (DPA). We show that our method is efficient and robust to detect all types of transitions in a generic manner. A specific detector for each type of transition of interest therefore becomes unnecessary. We illustrate our technique by two applications: a multiscale keyframe selection and a generic shot boundaries detection.

Information-Theoretic Analysis of Electronic and Printed Document Authentication

Abstract: In this paper we consider the problem of document authentication in electronic and printed forms. We formulate this problem from the information-theoretic perspectives and present the joint source-channel coding theorems showing the performance limits in such protocols. We analyze the security of document authentication methods and present the optimal attacking strategies with corresponding complexity estimates that, contrarily to the existing studies, crucially rely on the information leaked by the authentication protocol. Finally, we present the results of experimental validation of the developed concept that justifies the practical efficiency of the elaborated framework.

Facial Image Compression Based on Structured Codebooks in Overcomplete Domain

Abstract: We advocate facial image compression technique in the scope of distributed source coding framework The novelty of the proposed approach is twofold: image compression is considered from the position of source coding with side information and, contrarily to the existing scenarios where the side information is given explicitly; the side information is created based on a deterministic approximation of the local image features We consider an image in the overcomplete transform domain as a realization of a random source with a structured codebook of symbols where each symbol represents a particular edge shape Due to the partial availability of the side information at both encoder and decoder, we treat our problem as a modification of the Berger-Flynn-Gray problem and investigate a possible gain over the solutions when side information is either unavailable or available at the decoder Finally, the paper presents a practical image compression algorithm for facial images based on our concept that demonstrates the superior performance in the very-low-bit-rate regime

Dual diffusion model of spreading activation for content-based image retrieval

Abstract: This paper introduces a content-based information retrieval method inspired by the ideas of spreading activation models. In response to a given query,the proposed approach computes document ranks as their final activation values obtained upon completion of a diffusion process. This diffusion process,in turn,is dual in the sense that it models the spreading of the query 's initial activation simultaneously in two similarity domains: low-level feature-based and high-level semantic.The formulation of the diffusion process relies on an approximation that makes it possible to compute the final activation as a solution to a linear system of differential equations via a matrix exponential without the need to resort to an iterative simulation.The latter calculation is performed efficiently by adapting a sparse routine based on Krylov sub-space projection method.The empirical performance of the described dual diffusion model has been evaluated in terms of precision and recall on the task of content-based digital image retrieval in query-by-example scenario. The obtained experimental results demonstrate that the proposed method achieves better overall performance compared to traditional feature-based approaches. This performance improvement is attained not only when both similarity domains are used, but also when a diffusion model operates only on the feature-based similarities.

The edge process model and its application to information-hiding capacity analysis

Abstract: In this paper, the problem of capacity analysis of data-hiding techniques in a game information-theoretic framework is considered. Capacity is determined by the stochastic model of the host image, by the distortion constraints, and by the side information about the watermarking channel state available at the encoder and at the decoder. The importance of the proper modeling of image statistics is emphasized, and for this purpose, a novel stochastic nonstationary image model is proposed that is based on geometrical priors, the so-called edge process model. Being mathematically simple and tractable, the edge process model outperforms the estimation-quantization (EQ) and spike process models in reference applications such as denoising. Finally, this model allows us to obtain a realistic estimate of maximal embedding rates, and in particular, it is shown that the expected capacity limit of real images is significantly lower than previously reported.

Information-theoretic analysis of steganalysis in real images

Abstract: In this paper we consider the problem of performance improvement of non-blind statistical steganalysis of additive steganography in real images. The proposed approach differs from the existing solutions in two main aspects:(a) a locally non-stationary Gaussian model is introduced via source splitting to represent the statistics of the cover image and (b)the detection of the hidden information is performed not from all but from those channels that allow to perform it with the required accuracy. We analyze the theoretically attainable bounds in such a framework and compare them to the corresponding limits of the existing state-of-the-art frameworks. The performed analysis demonstrates the superiority of the proposed approach.

Performance analysis of nonuniform quantization-based data hiding

Abstract: In this paper, we tackle the problem of performance improvement of quantization-based data-hiding in the middle-watermark-to-noise ratio (WNR) regime. The objective is to define the quantization-based framework that maximizes the performance of the known-host-state data-hiding in the middle-WNR taking into account the host probability density function (pdf). The experimental results show that the usage of uniform deadzone quantization (UDQ) permits to achieve higher performance than using uniform quantization (UQ) or spread spectrum (SS)-based data-hiding. The performance enhancement is demonstrated for both achievable rate and error probability criteria.

Asymmetrically informed data-hiding optimization of achievable rate for Laplacian host

Abstract: In data-hiding the issue of the achievable rate maximization is closely related to the problem of host interference cancellation. The optimal host interference cancellation relies on the knowledge of the host realization and the channel statistics (the additive white Gaussian noise (AWGN) variance) available at the encoder a priori to the transmission. The latter assumption can be rarely met in practical situations. Contrarily to the Costa set-up where the encoder is optimized for the particular state of the independent and identically distributed (i.i.d.) Gaussian attacking channel, we address the problem of asymmetrically informed data-hiding optimal encoder design assuming that the host interference probability density function (pdf) is an i.i.d. Laplacian and the channel variance lies on some known interval. The presented experimental results advocate the advantages of the developed embedding strategy.

Message Communications And Channel State Estimation For State Dependent Channels

Abstract: In this paper, we consider the problem of pure information transmission and channel state estimation via state dependent channels. We show that the knowledge of auxiliary random variable, used in the codebook construction of random binning techniques, is sufficient to perform the optimal channel state estimation. We compare the obtained results with optimal rate-distortion region obtained using more involved coding strategies based on hybrid random binning and uncoded transmission. This analysis is performed for the generalized Gel’fand-Pinsker formulation and Gaussian Costa setup.

Laplacian channel state estimation for state dependent channels

Abstract: In this paper, we extend the results for optimal transmission of the Gaussian channel state via the state-dependent channels to the communications of the Laplacian data. We derive a minimum mean square estimate (MMSE) of the stationary independent identically (i.i.d.) distributed Laplacian channel state corrupted by an additive white Gaussian noise (AWGN) and demonstrate that estimation accuracy is the same in both cases and does not depend on the channel state pdf. For transmission performance improvement we propose to decompose the Laplacian data using the paradygm of parallel source splitting. We show that for the case of infinite Gaussian mixture approximation of Laplacian source it is possible to significantly improve the performance of the considered communication protocol.

Tangible interactive system for document browsing and visualisation of multimedia data

Abstract: In this paper we introduce and develop a framework for document interactive navigation in multimodal databases. First, we analyze the main open issues of existing multimodal interfaces and then discuss two applications that include interaction with documents in several human environments, i.e., the so-called smart rooms. Second, we propose a system set-up dedicated to the efficient navigation in the printed documents. This set-up is based on the fusion of data from several modalities that include images and text. Both modalities can be used as cover data for hidden indexes using data-hiding technologies as well as source data for robust visual hashing. The particularities of the proposed robust visual hashing are described in the paper. Finally, we address two practical applications of smart rooms for tourism and education and demonstrate the advantages of the proposed solution.

Performance evaluation of a contextual news story segmentation algorithm

Abstract: The problem of semantic video structuring is vital for automated management of large video collections. The goal is to automatically extract from the raw data the inner structure of a video collection; so that a whole new range of applications to browse and search video collections can be derived out of this high-level segmentation. To reach this goal, we exploit techniques that consider the full spectrum of video content; it is fundamental to properly integrate technologies from the fields of computer vision, audio analysis, natural language processing and machine learning. In this paper, a multimodal feature vector providing a rich description of the audio, visual and text modalities is first constructed. Boosted Random Fields are then used to learn two types of relationships: between features and labels and between labels associated with various modalities for improved consistency of the results. The parameters of this enhanced model are found iteratively by using two successive stages of Boosting. We experimented using the TRECvid corpus and show results that validate the approach over existing studies.

E-capacity analysis of data-hiding channels with geometrical attacks

Abstract: In a data hiding communications scenario, geometrical attacks lead to a loss of reliable communications due to synchronization problems when the applied attack is unknown. In our previous work, information-theoretic analysis of this problem was performed for theoretic setups, i.e., when the length of communicated data sequences asymptotically approaches infinity. Assuming that the applied geometrical attack belongs to a set of finite cardinality, it is demonstrated that it does not asymptotically affect the achievable rate in comparison to the scenario without any attack. The main goal of this paper is to investigate the upper and lower bounds on the rate reliability function that can be achieved in the data hiding channel with some geometrical state. In particular, we investigate the random coding and sphere packing bounds in channels with random parameter for the case when the interference (channel state) is not taken into account at the encoder. Furthermore, only those geometrical transformations that preserve the input dimensionality and input type class are considered. For this case we are showing that similar conclusion obtained in the asymptotic case is valid, meaning that within the class of considered geometrical attacks the rate reliability function is bounded in the same way as in the case with no geometrical distortions.

On reversibility of random binning based data-hiding techniques: security perspectives

Abstract: Reversibility of data-hiding refers to the reconstruction of original host data at the decoder from the stego data. Previous works on the subject are concentrated on the reversibility of data-hiding techniques from multimedia perspectives. However, from the security point of view, that at our knowledge was not exploited in existing studies, reversibility could be used by an attacker to remove the complete trace of watermark data from the stego data in the sense of designing the worst case attack. Thus, the aim of this paper is to analyze the reversibility of data-hiding techniques based on random binning from the security perspectives.

Costa Problem Under Channel Ambiguity

Abstract: In this paper, we address the analysis of the Costa setup under channel uncertainty. Since the Costa setup was entirely considered under the Gaussian assumptions about host and channel statistics, we assume that the channel is an additive white Gaussian noise (AWGN) with unknown variance defined on some interval. Firstly, we solve the problem of average achievable rate optimization assuming that the distribution of noise variances is known. Secondly, we withdraw the previous assumption and consider that the variance distribution is unknown. The corresponding criteria are formulated and the performance of the Costa under these criteria is demonstrated.

Self-Embedding Data Hiding for Non-Gaussian State-Dependent Channels: Laplacian Case

Abstract: In this paper, we consider the problem of optimal self-embedding Lapalcian data hiding for the state-dependent channels. In particular, we propose to decompose the Laplacian data using the paradigm of parallel source splitting. Experimental validation confirms the efficiency of the proposed approach.