Keystroke dynamics, a behavioral biometric modality, offers promising applications in authentication and intrusion detection systems. However, the scarcity of publicly available datasets due to privacy concerns limits research progress. This paper presents a Generative Adversarial Network (GAN) framework to generate synthetic keystroke dynamics data that closely mimics real-world patterns. Using the DSL-StrongPasswordData dataset, we pre-process and normalize timing features and train a GAN with 100-dimensional latent space, LeakyReLU activations, and binary cross-entropy loss. We evaluated the synthetic data through visual comparisons (boxplots, t-SNE projections) and statistical tests (Kolmogorov-Smirnov), demonstrating that the generated distributions align with real data (p-value > 0.05 for key features). Our results highlight the potential of the GAN for sharing data that preserve privacy and increase training sets for keystroke-based models.

The detection of sexual explicit content in multimedia plays a critical role in digital forensic investigations, offering substantial benefits in both criminal justice and Cybersecurity contexts. Automated and manual identification of such content can aid in uncovering illegal activities, including the possession and distribution of child sexual abuse material (CSAM), human trafficking, and sexual harassment. As digital environments continue to evolve, integrating advanced technologies such as artificial intelligence and machine learning is essential to improve detection capabilities, ensure legal compliance, and uphold ethical standards. In order to contribute to this issue, we propose in this paper an original method for sexual explicit content detection in videos with light deep learning models for allowing a fast analysis and deployment. The proposed systems provide very good results ($>96 \%$of accuracy) compared to the state of the art on a significant dataset (LSPD). The proposed solution keeps a low processing time that is an important property for the digital investigation application.

According to the EU’s General Data Protection Regulation, cancelable biometrics (CB) are essential for protecting biometric templates by combining three important criteria: irreversibility, revocability, and unlinkability. Unfortunately, many works have demonstrated that the distance preserving property, inherent to CB transforms, has permitted to initiate similarity-based attack (SA). Similarity-based attack takes the information leakage between the original distance and the transformed distance and aims at reconstructing a nearby biometric feature, used to gain illegal access to the system. In this paper, we propose to mitigate the SA by mastering the attacker’s knowledge that can lead to its success. For the sake of generality, we reformulate SA for unordered set templates and propose a generalized particle swarm optimization strategy to launch the attack. We pointed out that the weak point allowing the SA to operate is the distance score provided by the matching module. To limit the amount of attacker’s knowledge, we propose a new matching strategy adapted to all template formats based on similarity ratio score. We have performed experiments and different comparisons on two common databases, from fingerprints and faces, and have proved at each time, the efficiency of the given countermeasure to the threatening SA. Furthermore, the security is discussed when the attacker’s knowledge is expanded by additional information as synthetic biometric features, which meant to approximate the initial research space. Recommendations are then given to alleviate such risks at the design level.

Humans generate lots of data in the cyberworlds by their behaviors on social networks, interactions with computers or smartphones (writing documents, capturing images,…). Analyzing digital traces from Internet, hard drives or computers is an important issue considering the amount of data to process and the associated applications (criminal investigation, recruitment,…). In this paper, we propose an open-source software platform for such a task. It embeds many high-level evaluated tools. These tools rely on recent advances in artificial intelligence and deep learning. Our goal is to facilitate digital investigations conducted by criminal experts, researchers in digital archives, or IT engineers. This platform embeds many tools designed to process a document in order to extract some knowledge (as for example, determining if the filetype has been corrupted). We illustrate its benefit through the analysis of a real memory dump from a hard drive.

Cyberworlds offer a vast quantity of knowledge and services on all topics for Internet users. The protection of children is an important issue on Internet and could be solved by detecting automatically explicit content. Another application is to facilitate digital forensic experts when analyzing media such as hard drives to detect child pornography content in criminal affairs. In this work, we focus on images and we study the efficiency of existing methods from the literature mainly based on machine learning and deep learning approaches. We apply a rigorous protocol with significant datasets in order to draw conclusions on the performance we can expect in real conditions. This study shows that this task is not really solved by existing tools. Moreover, the frontier of explicit content is also not always easy to define.

Fingerprint recognition is a common solution for user authentication in Cybersecurity. This paper deals with the context of the certification of fingerprint biometric systems. The increasing use of biometric systems makes their certification a mandatory step in their development to assess their behavior in a real situation use. It has been shown that certain parameters such as environmental conditions can have a significant impact on the performance of biometric systems. However, there are also non-controlled parameters that depend on the user’s state such as the quality of his biometric samples. In this paper, we propose a study that explores the performance of fingerprint systems across these parameters.

Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multi-instance system.

Ensuring security on biometric systems has always been a high priority concern. Certification of biometric systems involves the testing of the system’s performance and its resistance to spoof attacks. The anti-spoofing test implies the creation and scan of multiples physical spoofs. This requests laboratory expertise and high amount of time for spoofs creation. In this paper, we propose a new solution based on deep learning to translate genuine fingerprint images and transform them into what they would look like if they were created from known spoof materials usually involved in fingerprint spoofing tests. Digitally Synthetized Fingerprint Spoofs (DSFS) help to cover a larger number of spoofs materials than it would be possible to physically fabricate in a given time. Validation method shows that synthetized images are as good as real spoofs considering their quality.

User authentication is an important issue to guarantee the security of information systems. For users, this task is in general inconvenient and boring. We propose in this paper a new transparent user authentication based on face recognition. During the use of a laptop or a smartphone, face features are computed, protected and sent to a trust party server that measures at any time the confidence of the user being the legitimate one. Authentication is transparent for the user while ensuring his/her security and privacy. We show through experiments with the AR face dataset the benefit of the proposed approach.

By adopting keystroke dynamics, authentication applications can integrate advanced identity proofing technology for detecting fraud and prevent unauthorized access. However, understanding a user's keystroke dynamics behavior in real applications is a challenging task regarding that this behavior is notably changing over time. To mitigate this problem, we apply, in this paper, the long short-term memory (LSTM) model that recognizes a continuous sequences of keystroke dynamics to identify users of public datasets. We also consider the bidirectional long short-term memory (BLSTM) as it maintain information about the future data. Hence, collecting information about intra-class variations of the keystroke dynamics from both past and future data, is an interesting solution to our problem. The obtained results are promising since we obtained an accuracy rate over than 60% for both architectures when dealing with public databases.

Nowadays, users interact with computer systems. Behavioral biometrics consists of analyzing user's interactions for identification and verification applications. This approach could be very useful for enhancing security and improving user experience and many privacy concerns are also related. In this paper, we address the problem of user identification considering their behaviors. How efficient are classical machine learning methods on such data? What about deep learning approaches? We illustrate this work on two behavioral modalities namely human activity using smartphones and keystroke dynamics on a laptop. Since the accuracy rates of most behavioral biometrics modalities are lower than morphological ones, we consider two approaches for these modalities that can be represented as time series: classical machine learning and deep learning techniques. We intend to show that many algorithms can obtain very good performance for different modalities without any specific tuning to the considered modality. This comparative analysis allows us to show that behavioral biometrics can be used for security applications (i.e. who is accessing the company information system) but could be a privacy concern as a user could be identified while navigating on the Internet.

Biometrics testing has for objective to determine the performance of a biometric system in order to guarantee security and user experience requirements. Providing trust in biometric systems is a key for many manufacturers. The performance is usually measured through the computation of matching scores between legitimate and impostor samples from a given database. Different bias in particular those linked to the environmental conditions can modify the performance of a biometric system. In this paper, we study the impact of acquisition conditions on fingerprint systems considering at the same time the quality and accuracy. We defined an own-made database controlling the acquisition conditions and we observe the behavior of three different matchers on these biometric data. Experimental results allow us to quantity their impact on performance and draw conclusions for testing biometric systems.

International audience

International audience

Smartphones have become ubiquitous in everyday life, storing and generating a huge amount of sensitive personal data which makes them vulnerable to increasing security and privacy threats. While protecting smartphones has become a necessity, existing traditional authentication methods, which are mainly based on the use of PIN codes and passwords, are facing remarkable drawbacks. Behavioural biometricsbased authentication is an interesting alternative to ensure a better protection and a better usability. This paper presents a transparent behavioural authentication solution using smartphone calling habits data. tested on a dataset of 93 users with more than 16.000 samples and shows promising results while guarantying a high privacy protection.

Biometrics has for objective to identify or verify the identity of an individual based on morphological or behavioral characteristics. A biometric system can be attacked by presenting a biometric data to the capture subsystem with the goal of interfering it, that is called a presentation attack. Covid, panther, shadow monster and dragon are the investigated presentation attacks associated to the Doddington Zoo Menagerie (which classify users in different categories considering their performance behavior when using biometric systems). In this work, we examined the robustness of each genuine class of the biometric menagerie against the proposed presentation attacks. The achieved experiments are applied to the keystroke dynamics modality. Owing to the adaptive strategy, we depicted each genuine category that is most vulnerable to a specific presentation attack class. We find that the impact of covid, panther, shadow monster and dragon attempts are more pronounced when compared to chameleons, worms, doves and phantoms classes respectively. The obtained results, point out that adding imposter labels to Doddington zoo may lead to a better assessment of biometric authentication systems and promotes the interpretation of their performances.

The quality assessment of biometric samples is a crucial issue in biometrics, indeed, many studies showed its significant impact on the subsequent performance of the biometric system. Many metrics have been proposed and studied in the literature in order to quantify their usefulness. In this paper, we propose to merge different metrics in order to improve the utility estimation of the quality assessment. We use the enrollment selection validation approach in order to compute the utility estimation of the fused metrics. We show the efficiency of the proposed approach comparing with 7 well known metrics on the 12 FVC datasets and 5 synthesized SFinGE-based databases with two matching algorithms. Experimental results show a good improvement on the fused metric to better qualify the quality of digital fingerprints. Those results demonstrate the effectiveness of the approach.

Nowadays, there are large country-sized fingerprint databases for identification, border access controls and also for Visa issuance procedures around the world. Fingerprint indexing techniques aim to speed up the research process in automatic fingerprint identification systems. Therefore, several preselection, classification and indexing techniques have been proposed in the literature. Even if, the proposed systems have been evaluated with different experimental protocols, it is difficult to assess their relative performance. The main objective of this paper is to provide a comparative study of fingerprint indexing methods using a common experimental protocol. Four fingerprint indexing methods, using naive, cascade, matcher and Minutiae Cylinder Code (MCC) approaches are evaluated on FVC databases from the Fingerprint Verification Competition (FVC) using the Cumulative Matches Curve (CMC) and the computing time required. Our study shows that MCC gives the best compromise between identification accuracy and computation time.

The attacks considered for keystroke dynamics study especially adaptive strategies have commonly treated impersonation attempts known as zero-effort attacks. These attacks are generally the acquisition of other users of the same database while typing the same password without intending to impersonate the genuine user account. To deal with more realistic scenarios, we are interested in this paper to study the robustness of an adaptive strategy against four types of imposter attacks: zero-effort, spoof, playback and synthetic applied to the WEBGREYC database. Experimental results show that 1) playback and synthetic attacks are the most dangerous and increase the EER rates compared to the other attacks; 2) we also find that the impact of these attacks is more pronounced when the percentages of imposter samples are greater than those of genuine ones; 3) the spoof attacks achieve alarmingly higher FMR, FNMR, and EER rates compared to zero-effort impostor attacks; 4) FMR, FNMR, and EER are higher when the percentage of attacks increases; 5) the attacks belonging to the same user are more dangerous than those of different users in particular when the percentage of the attacks increases. In light of our results, we point out that the traditional attacks considered in research on keystroke-based authentication must evolve according to the evolution of the attacks of nowadays password-based applications.

Many studies propose strong user authentication based on biometric modalities. However, they often either, assume a trusted component, are modality-dependant, use only one biometric modality, are reversible, or does not enable the service to adapt the security on-the-fly. A recent work introduced the concept of Personal Identity Code Respecting Privacy (PICRP), a non-cryptographic and non-reversible signature computed from any arbitrary information. In this paper, we extend this concept with the use of Keystroke Dynamics, IP and GPS geo-location by optimizing the pre-processing and merging of collected information. We demonstrate the performance of the proposed approach through experimental results and we present an example of its usage.

Cyber-attacks have spread all over the world to steal information such as trade secrets, intellectual property and banking data. Facing the danger of the insecurity of saved data (personal, professional, official, etc.), keystroke dynamics was proposed as an interesting, non-intrusive, inexpensive, permanent and weakly constrained solution for users. Based on the typing rhythm of users, it improves logical access security. Nevertheless, it was demonstrated that such an authentication mechanism would need a larger number of samples to enroll the typing characteristics of users. Moreover, these registered characteristics generally undergo aging effects after a time span. Different solutions have been suggested to remedy these variability problems, including template adaptation. In this paper, we propose a double serial adaptation strategy that considers a single-capture-based enrollment process. When using the authentication system, the template of users and the decision/adaptation thresholds are updated. Experimental results on three public keystroke dynamics datasets show the benefits of the proposed method.

Template protection is a crucial issue in biometrics. Many algorithms have been proposed in the literature among secure computing approaches, crypto-biometric algorithm and feature transformation schemes. The BioHashing algorithm belongs to this last category and has very interesting properties. Among them, we can cite its genericity since it could be applied on any biometric modality, the possible cancelability of the generated BioCode and its efficiency when the secret is not stolen by an impostor. Its main drawback is its weakness face to a combined attack (false acceptance with the stolen secret scenario). In this paper, we propose a transformation-based biometric template protection scheme as an improvement of the BioHashing algorithm where the projection matrix is generated by combining the secret and the biometric data. Experimental results on three biometric modalities, namely digital fingerprint, finger knuckle print and hands vein images, show the benefits of the proposed method face to attacks while keeping a good efficiency.

Biometric systems are currently widely used in many applications to control and verify individual's identity. Keystroke dynamics modality has been shown as a promising solution that would be used in many applications such as e-payment and banking applications. However, such systems suffer from several performance limitations (such as cross-devices problem) that prevent their widespread of use in real applications. The objective of this paper is to provide researchers and developers with a public touchscreen-based benchmark collected using a mobile phone and a tablet (both portrait and landscape orientation each). Such a benchmark can be used to assess keystroke-based matching algorithms. Furthermore, It is mainly developed to measure the robustness of keystroke matching algorithms vis-'a-vis cross-devices and orientation variations. An online visualizer for the database is also given to researchers allowing them to visualize the acquired keystroke signals.

Template protection is a crucial issue in biometrics. Many algorithms have been proposed in the literature among secure computing approaches, crypto-biometric algorithm and feature transformation schemes. The BioHashing algorithm belongs to this last category and has very interesting properties. Among them, we can cite its genericity since it could be applied on any biometric modality, the possible cancelability of the generated BioCode and its efficiency when the secret is not stolen by an impostor. Its main drawback is its weakness face to a combined attack (zero effort with the stolen secret scenario). In this paper, we propose a transformation-based biometric template protection scheme as an improvement of the BioHashing algorithm where the projection matrix is generated by combining the secret and the biometric data. Experimental results on two biometric modalities, namely digital fingerprint and finger knuckle print images, show the benefits of the proposed method face to attacks while keeping a good efficiency.

Regarding the fact that individuals have different interactions with biometric authentication systems, several techniques have been developed in the literature to model different users categories. Doddington Zoo is a concept of categorizing users behaviors into animal groups to reflect their characteristics with respect to biometric systems. This concept was developed for different biometric modalities including keystroke dynamics. The present study extends this biometric classification, by proposing a novel adaptive strategy based on the Doddinghton Zoo, for the recognition of the user's keystroke dynamics. The obtained results demonstrate competitive performances on significant keystroke dynamics datasets.

Biometrics is an emerging technology more and more present in our daily life. However, building biometric systems requires a large amount of data that may be difficult to collect. Collecting such sensitive data is also very time consuming and constrained, s.a. GDPR legislation. In the case of keystroke dynamics, existing databases have less than 200 users. For these reasons, we aim at generating a keystroke dynamics synthetic dataset. This paper presents the generation of keystroke data from known users as a first step towards the generation of synthetic datasets, and could also be used to impersonate users' identity.

Various applications on Internet require information on users, to verify their right to access services (verification of identity proofs s.a. passwords), to avoid attacks (s.a. paedophilia, profile usurpations), or to give trust to users (e.g. in social networks). In this paper, we introduce a method to generate (non-cryptographics) identity-based signatures computed from 1) collection of data from user biomet-rics, computer configuration, web browser fingerprinting, 2) data pre-processing, 3) protection of personal information through generation of a binary code (our signature). We illustrate the benefits of the proposed method with preliminary results on real personal information.

International audience

Securing personal, professional and even official data is a very critical issue nowadays, giving that these informations are safeguarded in different devices (mobile, computer) and various accounts (social networks, e-mails). To protect them from unauthorized access, users generally are asked to use passwords. But using only this authentication solution is no longer efficient against hacker attacks. Keystroke dynamics is a biometric promising modality that guarantees the recognition of the user's characteristics; his typing manner on the keyboard. Regarding that the typing rhythm of the user changes over time, adaptive biometric strategies help to take into consideration these variations during the authentication system. In this paper we classify users into multiple categories according to Doddington Zoo classification. Afterwards, we apply an adaptive strategy specific to each category of users. The achieved experiments demonstrate that an update strategy specific to the user class significantly improves the obtained performances.

Smart cards are involved in most of activities, and they gather and record plenty of personal data. A manual interpretation of these raw data is difficult without specifications. This task becomes really tedious applied to plenty of devices. The paper introduces the first method to automatically retrieve textual information from memory dumps of smart cards. Given the data structure and encoding are assumed to be unknown, the method is based on text statistics and characteristics of smart cards to discard false positives. The experiments performed on more than 350 memory dumps revealed that the method can automatically retrieve more than 99% of textual information available in a dump, while keeping the false positive rate as low as 5.5%.

Fake identities and identity theft are issues whose relevance is increasing in the social network domain. This paper deals with this problem by proposing an innovative approach which combines a collaborative mechanism implementing a trust graph with keystroke-dynamic-recognition techniques to trust identities. The trust of each node is computed on the basis of neighborhood recognition and behavioral biometric support. The model leverages the word of mouth propagation and a settable degree of redundancy to obtain robustness. Experimental results show the benefit of the proposed solution even if attack nodes are present in the social network.

International audience

Fingerprint analysis is a very important issue in biometry. The minutiae representation of a fingerprint is the most used modality to identify people or authorize access when using a biometric system. In this paper, we propose some features based on triangle parameters from the Delaunay triangulation of minutiae. We show the benefit of these features to recognize the type of a fingerprint without any access to the associated fingerprint image.

One time passwords are commonly used for authentication purposes in electronic transactions. Nevertheless, providing such a one time password is not really a strong authentication proof %as it can be given by an impostor. because the token generating the passwords can be given by an impostor. In order to cope with this problem, biometric recognition is more and more employed. Even if biometric data are strongly linked with the user, their revocability nor diversity is possible, without an adapted post-processing. Biometric template protection schemes, including the BioHashing algorithm, are used to manage the underlying privacy and security issues. These schemes are used for the protection of several biometric modalities, but are not necessary adapted for all of them. In this paper, we propose a new protocol combining protected biometric data and a classical synchronous one time password to enhance the security of user authentication while preserving usability and privacy. Behavioral biometrics is used to provide a fast and a usable solution for users. We show through experimental results the efficiency of the proposed method.

The trust provided by authentication systems is commonly expressed with a Level of Assurance (LoA see 3). If it can be considered as a first process to simplify the expression of trust during the authentication step, it does not handle all the aspects of the authentication mechanism and especially it fails to integrate continuous authentication systems. In this paper, we propose a model based on the Dempster Shafer theory to merge continuous authentication system with more traditional static authentication scheme and to assign a continuous trust level to the current LoA. In addition, this method is proved to be compliant with the LoA frameworks.

Smart cards are tamper resistant devices but vulnerabilities are sometimes discovered. We address in this paper the security and the functional testing of embedded applications in smart cards. We propose an original methodology for the evaluation of applications and we show its benefit by comparing it to a classical certification process. The proposed method is based on the observation of the APDU (Application Protocol Data unit) communication with the smart card. Some specific properties are verified as a complementary method in the evaluation process and allows the on-the-fly detection of an anomaly and the reasons that triggered this anomaly during the test. Here are presented two uses of this method: a simple use to illustrate the use of properties to verify an implementation of an application and a more complex illustration by applying the fuzzing method to show what we can obtain with the proposed approach, i.e. an analysis of an anomaly.

In this paper, we deal with the proof of ownership or legitimate usage of a digital content, such as an image, in order to tackle the illegitimate copy. The proposed scheme based on the combination of the watermarking and cancelable biometrics does not require a trusted third party, all the exchanges are between the provider and the customer. The use of cancelable biometrics allows us to provide a privacy compliant proof of identity. We illustrate the robustness of this method against intentional and unintentional attacks of the watermarked content.

The electronic payment transaction involves the use of a smart card. A card application is a software, corresponding to standards and non-proprietary and proprietary specifications, and is stored in the smart card. Despite increased security with Euro pay Mastercard Visa (EMV) specifications, attacks still exist due to anomalies in the card application. The validation of the card application enables the detection of any anomaly, improving the overall security of electronic payment transactions. Among the different ways of validating a card application, we can use the verification of required behaviors. These behavior can be materialized as properties of commands sent by the terminal and responses from the smart card, using the Application Protocol Data Unit (APDU) from the ISO/IEC 7816 standard [1]. However, the creation of these behaviors is complicated. We propose in this article a way to automatically create such behaviors by using a genetic algorithm technique.

Image quality is an important factor for automated fingerprint identification systems (AFIS) because the matching performance could be significantly affected by poor quality samples. Most of the existing studies mainly focus on calculating a quality index via either a single feature or a combination of multiple features, and some others achieve this purpose with learning approaches which may depend on a prior-knowledge of matching performance. In this paper, a general framework for estimating fingerprint image quality is proposed by fusing features in segmentation phase. The quality index is indicated by a ratio of the pixel number of the integrated foreground area to the size (pixel number) of the fingerprint image. The potential advantage of this framework is that it could be improved by integrating other segmentation approaches or quality features rather than fusing them in a more complicated manner. The experiment is performed with several fingerprint datasets created via different sensors. Experimental results obtained from a dual evaluation approach demonstrate the validity of the proposed method in improving the overall performance.

Nowadays, when someone wants to make a payment with a smartcard, the user has to enter a pin code to be identified. Only biometrics is able to authenticate a user; yet biometric information is sensitive. To ensure the security and privacy of biometric data, OCC (On-Card-Comparison) has been proposed. This approach consists in storing biometric data in a secure zone on a smartcard and computing the verification decision in a Secure Element (SE). The purpose of this paper is to propose an evaluation platform for testing biometric systems such as the analysis of performance and security on biometric OCC. Based on two examples, we illustrate its different uses in an operationnal context. The first example focus on the ”Quality module” which allows to choose the enrollment by considering the fingerprint quality with one proposed metric. The second one addresses the minutiae reduction of the fingerprint template when the number of minutiae is higher than expected by the OCC.

This article proposes a new quality assessment method of fingerprint, represented by only a set of minutiae points. The proposed quality metric is modeled with the convex-hull and Delaunay triangulation of the minutiae points. The validity of this quality metric is verified on several Fingerprint Verification Competition (FVC) databases by referring to an image-based metric from the state of the art (considered as the reference). The experiments of the utility-based evaluation approach demonstrate that the proposed quality metric is able to generate a desired result. We reveal the possibility of assessing fingerprint quality when only the minutiae template is available.

Biometric sample quality assessment approaches are generally designed in terms of utility property due to the potential difference between human perception of quality and the biometric quality requirements for a recognition system. This study proposes a utility based quality assessment method of fingerprints by considering several complementary aspects: 1) Image quality assessment without any reference which is consistent with human conception of inspecting quality, 2) Textural features related to the fingerprint image and 3) minutiae features which correspond to the most used information for matching. The proposed quality metric is obtained by a linear combination of these features and is validated with a reference metric using different approaches. Experiments performed on several trial databases show the benefit of the proposed fingerprint quality metric.

Biometric cryptosystems represent emerging techniques for biometric template protection. These cryptosystems are vulnerable to different types of attacks, as brute force attacks or correlation attacks if several templates are compromised. Another biometric security issue comes from certain biometric data (as fingerprint or face image) that can leave traces, but are, in the same time, the most commonly biometric modalities used in mobile security. In this paper, a new attack based on the alteration of original user data is investigated on fuzzy Vault biometric cryptosystems. We assume that the attacker uses a modified version of the real user image to gain unauthorized access to the system (mobile phone). Experimental results carried out using fingerprint and face modalities show that this assumption has serious impact on the security of this type of biometric cryptosystems.

E-health information systems give rise to many security and privacy concerns. Security of these systems involves a large amount of sensitive data shared by several actors, such as doctors or nurses in various institutions. However, the privacy preserving issue, including data minimization and data sovereignty, is not necessary treated. This paper presents an e-health infrastructure intended to minimize personal data disclosure, with an access to right system based on a secret sharing. We analyze the security of the proposed infrastructure in accordance with medical constraints through contactless transactions.

The concept of authentication assurance traditionally refers to the robustness of methods and mechanisms for user authentication, including the robustness of initial registration and provisioning of user credentials, as well as the robustness of mechanisms that enforce user authentication during operation. However, the user is not the only party that needs to be authenticated to ensure security of online transactions. In fact, online service provision always involves two parties, typically the user on the client side and the service provider on the server side, so that mutual authentication between the two sides is required. In contrast to the unilateral focus on user authentication by industry and academia, it is in fact equally important for the user to correctly authenticate the service provider. Unfortunately, little attention is paid to the problem of correctly authentication the service provider. This paper proposes a framework for server and service provider authentication assurance, similarly to frameworks for user authentication assurance that have already been specified, or are currently under development by many national governments.

The storage of fingerprints is an important issue as this biometric modality is more and more deployed for real applications. The a prori impossibility to revoke a biometric template (like a password) in case of theft, is a major concern for privacy reasons. We propose in this paper a new method to secure fingerprint minutiae templates by storing a bio code while keeping good recognition results. We show the efficiency of the method in comparison to some published methods for different scenarios.

We propose in this paper a low cost multimodal biometric system combining keystroke dynamics and 2D face recognition. The objective of the proposed system is to be used while keeping in mind: good performances, acceptability, and espect of privacy. Different fusion methods have been used (min, max, mul, svm, weighted sum configured with genetic algorithms, and, genetic programming) on the scores of three keystroke dynamics algorithms and two 2D face recognition ones. This multimodal biometric system improves the recognition rate in comparison with each individual method. On a chimeric database composed of 100 individuals, the best keystroke dynamics method obtains an EER of 8.77%, the best face recognition one has an EER of 6.38%, while the best proposed fusion system provides an EER of 2.22%.

We propose in this paper to study different color spaces for representing an image for the face authentication application. We used a generic algorithm based on a matching of keypoints using sift descriptors computed on one color component. Ten color spaces have been studied on four large and significant benchmark databases (ENSIB, FACES94, AR and FERET). We show that all color spaces do not provide the same efficiency and the use of the color information allows an interesting improvement of verification results.

We explore a location based approach for behavior modeling and abnormality detection. In contrast to the conventional object based approach where an object may first be tagged, identified, classified, and tracked, we proceed directly with event characterization and behavior modeling at the pixel(s) level based on motion labels obtained from background subtraction. Since events are temporally and spatially dependent, this calls for techniques that account for statistics of spatiotemporal events. Based on motion labels, we learn co-occurrence statistics for normal events across space-time. For one (or many) key pixel(s), we estimate a co-occurrence matrix that accounts for any two active labels which co-occur simultaneously within the same spatiotemporal volume. This co-occurrence matrix is then used as a potential function in a Markov random field (MRF) model to describe the probability of observations within the same spatiotemporal volume. The MRF distribution implicitly accounts for speed, direction, as well as the average size of the objects passing in front of each key pixel. Furthermore, when the spatiotemporal volume is large enough, the co-occurrence distribution contains the average normal path followed by moving objects. The learned normal co-occurrence distribution can be used for abnormal detection. Our method has been tested on various outdoor videos representing various challenges.

We present in this paper a human detection system for the analysis of video sequences. We perform first a foreground detection with a Gaussian background model. A tracking step based on connected components analysis combined with feature points tracking allows to collect information on 2D displacements of moving objects in the image plane and so to improve the performance of our classifier. A classification based on a cascade of boosted classifiers is used for the recognition. Moreover, we present the results of two comparative studies which concern the background subtraction and the classification steps. Algorithms from the state of the art are compared in order to validate our technical choices. We finally present some experimental results showing the efficiency of the proposed algorithm.

Many object descriptors have been proposed in the state of the art. For many reasons (occlusion, point of view, acquisition conditions...), local descriptors have a better robustness for image understanding applications. The goal of this paper is to make a comparative study of eight recent local descriptors. The objective is here to quantify their ability to generate automatically an object taxonomy. In order to answer this question, we use the Caltech256 benchmark which provides a large object taxonomy used as reference. This study shows that SIFT, differential invariants and shape context descriptors are the best ones to achieve this goal.

International audience

This paper analyzes the benefit and the limitations of using a particular biometric technology "namely face authentication" for banking applications. We present first the general concepts of banking. We propose a method in order to replace the PIN code authentication by using biometrics data. Biometric authentication is then detailed. A face recognition method we developed is presented revealing as itself as a biometric candidate solution. We show the benefit and limits of this approach to be used in a real industrial context.

Locating moving objects in a video sequence is the first step of many computer vision applications. Among the various motion-detection techniques, background subtraction methods are commonly implemented, especially for applications relying on a fixed camera. Since the basic inter-frame difference with global threshold is often a too simplistic method, more elaborate (and often probabilistic) methods have been proposed. These methods often aim at making the detection process more robust to noise, background motion and camera jitter. In this paper, we present commonly-implemented background subtraction algorithms and we evaluate them quantitatively. In order to gauge performances of each method, tests are performed on a wide range of real, synthetic and semi-synthetic video sequences representing different challenges.

We propose in this paper a face authentication method based on a similarity measure. The SIFT descriptor is used to define some interest keypoints characterized by an invariant parameter. A graph is then built where nodes correspond to these keypoints. We model the authentication problem as a graph matching process. Experimental results on the AR database show an EER equals to 12% with only one image used for the enrollment and with images simulating real conditions.

In this paper, we put forward a novel fusion framework that mixes together label fields instead of observation data as is usually the case. Our framework takes as input two label fields: a quickly estimated and to-be-refined segmentation map and a spatial region map that exhibits the shape of the main objects of the scene. These two label fields are fused together with a global energy function that is minimized with a deterministic iterative conditional mode algorithm. As explained in the paper, the energy function may implement a pure fusion strategy or a fusion-reaction function. In the latter case, a data-related term is used to make the optimization problem well posed. We believe that the conceptual simplicity, the small number of parameters, the use of a simple and fast deterministic optimizer that admits a natural implementation on a parallel architecture are among the main advantages of our approach. Our fusion framework is adapted to various computer vision applications among which are motion segmentation, motion estimation and occlusion detection.

This paper presents a novel region-based approach for detecting occlusion between two consecutive frames. Based on a generalization of Marr and Poggio’s uniqueness assumption, the explicit goal of our method is to reduce the number of false positives while optimizing the hit rate. To do so, our method relies on a fusion procedure that blends together two segmentation maps: one pre-estimated occlusion binary map and one color segmentation map. While the occlusion map is obtained after a simple thresholding procedure, the color segmentation map is obtained with an unsupervised Markovian approach. Assuming that the color segmentation regions exhibit more precise edges, the occlusion areas are iteratively modified to fit the colorregion shapes. Since our method has been entirely implemented on a parallel architecture (a Graphics Processor Unit), its processing times are remarkably low. Our method is compared with other occlusion approaches both quantitatively and qualitatively on scenes that represent different challenges. 1

This paper presents a new tool for the evaluation of ultrasound image compression. The goal is to measure the image quality as easily as with a statistical criterion, and with the same reliability as the one provided by the medical assessment. An initial experiment is proposed to medical experts and represents our reference value for the comparison of evaluation criteria. Twenty-one statistical criteria are selected from the literature. A cumulative absolute similarity measure is defined as a distance between the criterion to evaluate and the reference value. A first fusion method based on a linear combination of criteria is proposed to improve the results obtained by each of them separately. The second proposed approach combines different statistical criteria and uses the medical assessment in a training phase with a support vector machine. Some experimental results are given and show the benefit of fusion.

2D Zernike moments belong to the useful object invariant descriptors which have been successfully applied in pattern recognition tasks. The main problem of using Zernike moments invariants is that they are not able to discriminate two objects having the same shape. In this paper, an approach based on Zernike moments applied on color images is proposed. A support vector machine is used for object classification. For object segmentation, the connected component labeling algorithm is used. Compared with the classical method, this approach shows higher accuracy in object recognition. Some experimental results on the COIL-100 database are presented.

This paper presents a new tele-operated robotic chain for real-time ultrasound image acquisition and medical diagnosis. This system has been developed in the frame of the Mobile Tele-Echography Using an Ultralight Robot European Project. A light-weight six degrees-of-freedom serial robot, with a remote center of motion, has been specially designed for this application. It holds and moves a real probe on a distant patient according to the expert gesture and permits an image acquisition using a standard ultrasound device. The combination of mechanical structure choice for the robot and dedicated control law, particularly nearby the singular configuration allows a good path following and a robotized gesture accuracy. The choice of compression techniques for image transmission enables a compromise between flow and quality. These combined approaches, for robotics and image processing, enable the medical specialist to better control the remote ultrasound probe holder system and to receive stable and good quality ultrasound images to make a diagnosis via any type of communication link from terrestrial to satellite. Clinical tests have been performed since April 2003. They used both satellite or Integrated Services Digital Network lines with a theoretical bandwidth of 384 Kb/s. They showed the tele-echography system helped to identify 66% of lesions and 83% of symptomatic pathologies.

This paper describes an industrial system which realizes the cooperation of a robot and a vision system for the quality control of manufactured pieces. We show in this paper its ability to be tele-operated from a distant computer for convenience of development or for a new parameterization of the system. This system is described and its efficiency is demonstrated through an example of use for industrial metallic pieces.

In this communication, we propose a new approach which enables to fusion either the results of several segmentation methods of a same image or the different results in the case of a multi-components image. The developed method is based on a genetic algorithm approach which allows to combine segmentation results by taking into account their quality through an evaluation criterion. This criterion provides to quantify a segmentation result without any a priori knowledge such as the ground truth. This approach is applied to segment multi-components images by combining the segmentation results of each component. We show the efficiency of the method through some experimental results on several images.

We propose in this communication an unsupervised clustering method called MLBG based upon the K-means algorithm. The originality of this method lies in the automatic determination of the number of clusters by calling into question an intermediate result. This method also enables to improve the different steps in the K-means algorithm. We show the efficiency of the MLBG method through some experimental results and we demonstrate the usefulness of the technique for image segmentation.

Texture analysis is an important problem in image processing because it conditions the quality of image segmentation and interpretation. We propose in this communication a texture model which is invariant by rotation and whose parameters allow to characterize at the same time the type of texture and its tonal primitive. The originality of the model proposed lies in the use of the Wold decomposition to model the 1D normalized autocovariance. This function is computed from the 2D normalized autocovariance of a texture. Finally, parameters of the model are estimated by using a genetic algorithm. Experimental results on textures from the Brodatz album and synthetic textures show a modeling error lower than 0.06.