Biometric Antispoofing Methods: A Survey in Face Recognition <s> II. BIOMETRIC ANTI-SPOOFING <s> A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field. Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems. This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators. <s> BIB001 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> II. BIOMETRIC ANTI-SPOOFING <s> Signature recognition has a long history of usage in authentication of transactions and legal contracts and hence is easily accepted by users in a variety of applications. However, the problem of skilled forgeries is an important challenge that needs to be overcome before signature recognition systems will become viable in unsupervised authentication systems. In this paper, we present a multimodal approach to forgery detection, where a physiological trait, the face of the signing person, is used to validate the signature. Methods of normalizing and combining the matching scores from the individual modalities are investigated. Test results of the system on a database of 100 users is presented. The system achieves an equal error rate of 2.2% in the presence of high quality skilled forgeries and could detect all the skilled forgeries at a genuine acceptance rate of 75%. <s> BIB002 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> II. BIOMETRIC ANTI-SPOOFING <s> The automatic vitality detection of a fingerprint has become an important issue in personal verification systems based on this biometric. It has been shown that fake fingerprints made using materials like gelatine or silicon can deceive commonly used sensors. Recently, the extraction of vitality features from fingerprint images has been proposed to address this problem. Among others, static and dynamic features have been separately studied so far, thus their respective merits are not yet clear; especially because reported results were often obtained with different sensors and using small data sets which could have obscured relative merits, due to the potential small sample-size issues. In this paper, we compare some static and dynamic features by experiments on a larger data set and using the same optical sensor for the extraction of both feature sets. We dealt with fingerprint stamps made using liquid silicon rubber. Reported results show the relative merits of static and dynamic features and the performance improvement achievable by using such features together. <s> BIB003 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> II. BIOMETRIC ANTI-SPOOFING <s> Tesis doctoral inedita. Universidad Autonoma de Madrid, Escuela Politecnica Superior, noviembre de 2009 <s> BIB004 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> II. BIOMETRIC ANTI-SPOOFING <s> Although the market for biometric technologies is expanding, the existing biometric systems present still some issues that the research community has to address. In particular, ::: in adverse environmental conditions (e.g., low quality biometric signals), where the error rates increase, it is necessary to create more robust and dependable systems. In the literature on biometrics, the integration of multiple biometric sources has been successfully used to improve the recognition accuracy of the unimodal biometric systems. Multibiometric ::: systems, by exploiting more information, such as different biometric traits, multiple ::: samples, multiple algorithms, make more reliable the biometric authentication. Benefits of multibiometrics depend on the diversity among the component matchers and also, on the competence of each one of them. In non-controlled conditions of data acquisition, there is a degradation of biometric signal quality that often causes a significant deterioration of recognition performance. It is intuitive the concept that, the classifier having the higher quality is more credible than a classifier operating on noisy data. Then, researchers started to propose quality-based fusion schemes, where the quality measures of the samples have ::: been incorporated in the fusion to improve performance. Another promising direction in multibiometrics is to estimate the decision reliability of the component modality matcher based on the matcher output itself. An interesting open research issue concerns how to estimate the decision reliability and how to exploit this information in a fusion scheme. ::: From a security perspective, a multimodal system appears more protected than its unimodal components, since spoofing two or more modalities is harder than spoofing only one. ::: However, since a multimodal system involves different biometric traits, it offers a higher number of vulnerable points that may be attacked by a hacker who may choice to fake only a subset of them. Recently, researchers investigated if a multimodal system can be deceived by spoofing only a subset but not all the fused modalities. The goal of this thesis is to improve the performance of the existing integration mechanisms in presence of degraded ::: data and their security in presence of spoof attacks. Our contribution concerns three important issues: 1) Reducing verification errors of a fusion scheme at score level based on the statistical Likelihood Ratio test, by adopting a sequential test and, when the number of training samples is limited, a voting strategy. 2) Addressing the problem of identification errors, by setting up a predictor of errors. The proposed predictor exploits ranks and scores generated by the identification operation and can be effectively applied in a multimodal scenario. 3) Improving the security of the existing multibiometric systems against spoof attacks which involve some but not all the fused modalities. Firstly, we showed that in such a real scenario performance of the system dramatically decrease. Then, for the fingerprint modality, we proposed a novel liveness detection algorithm which combines perspirationand morphology-based static features. Finally, we demonstrated that, by incorporating our algorithm in the fusion scheme, the multimodal system results robust in presence of spoof ::: attacks. <s> BIB005 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> II. BIOMETRIC ANTI-SPOOFING <s> Multimodal biometric systems have consistently presented better recognition rates when compared to the unimodal systems that compose them. A common claim is that they also provide higher security when compared to unimodal systems since an intruder would have to successfully break into more than one biometrical system. We argue that this may not be true due to two main reasons: a multimodal system has a higher number of vulnerable points that may be explored by an intruder, and an intruder may break the multimodal system by attacking only a subset of the unimodal systems. In particular, we investigate a multimodal system composed of face and fingerprint under different spoof attack scenarios. In this case, a forger may choose to spoof the face or fingerprint traits. We empirically show that the false acceptance rate increases dramatically when either mode is spoofed, which means that an intruder may be falsely authenticated by spoofing only one mode. <s> BIB006 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> II. BIOMETRIC ANTI-SPOOFING <s> Abstract Biometrics is essentially a pattern recognition system that recognizes an individual using their unique anatomical or behavioral patterns such as face, fingerprint, iris, signature etc. Recent researches have shown that many biometric traits are vulnerable to spoof attacks. Moreover, recent works showed that, contrary to a common belief, multimodal biometric systems in parallel fusion mode can be intruded even if only one trait is spoofed. However, most of the results were obtained using simulated spoof attacks, under the assumption that the spoofed and genuine samples are indistinguishable, which may not be true for all biometric traits. In addition, so far vulnerability of multimodal biometric systems in serial fusion mode against spoof attacks has not been investigated. These issues raise a demand to investigate the robustness of multimodal systems under realistic spoof attacks. In this paper, we empirically investigate the performance of serial and parallel biometric fusion modes under realistic spoof attacks. Preliminary empirical results on real biometric systems made up of face, fingerprint and iris confirm that multimodal biometric systems in both fusion modes are not intrinsically robust against spoof attacks as believed so far. In particular, multimodal biometric systems in serial fusion mode can be even less robust than systems in parallel mode. We also experimentally found that incorporating the biometric sample quality in biometric fusion increases the robustness of the multimodal systems against spoof attacks. In the end, we study the trade-off between performance and robustness of the biometric systems under spoof attacks. <s> BIB007 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> II. BIOMETRIC ANTI-SPOOFING <s> Recent works have investigated the robustness to spoofing attacks of multi-modal biometric systems in parallel fusion mode. Contrary to a common belief, it has been shown that they can be cracked by spoofing only one bio-metric trait. Robustness evaluation of multi-modal systems in serial fusion mode has not yet been investigated, instead. Thus, the aim of this paper is to comparatively evaluate the robustness of multi-modal systems, in serial and parallel fusion modes, under spoofing attacks. In particular, we empirically investigate the vulnerability of serial and parallel fusion of face and fingerprint biometrics to real spoofing attacks. Our results show that multi-modal systems in both fusion modes are vulnerable to attacks against a single bio-metric trait. On the other hand, they show that the serial fusion mode can attain a favorable trade-off between performance, verification time, and robustness against spoofing attacks. <s> BIB008 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> II. BIOMETRIC ANTI-SPOOFING <s> A biometric system is essentially a pattern recognition system being used in ad-versarial environment. Since, biometric system like any conventional security system is exposed to malicious adversaries, who can manipulate data to make the system ineffective by compromising its integrity. Current theory and de- sign methods of biometric systems do not take into account the vulnerability to such adversary attacks. Therefore, evaluation of classical design methods is an open problem to investigate whether they lead to design secure systems. In order to make biometric systems secure it is necessary to understand and evalu-ate the threats and to thus develop effective countermeasures and robust system designs, both technical and procedural, if necessary. Accordingly, the extension ::: of theory and design methods of biometric systems is mandatory to safeguard the security and reliability of biometric systems in adversarial environments. In ::: this thesis, we provide some contributions towards this direction. Among all the potential attacks discussed in the literature, spoof attacks are one of the main threats against the security of biometric systems for identity ::: recognition. Multimodal biometric systems are commonly believed to be in-trinsically more robust to spoof attacks than systems based on a single biomet-ric trait, as they combine information coming from different biometric traits. ::: However, recent works have question such belief and shown that multimodal systems can be misled by an attacker (impostor) even by spoofing only one of the biometric traits. Therefore, we first provide a detailed review of state-of-the-art works in multimodal biometric systems against spoof attacks. The scope ofstate-of-the-art results is very limited, since they were obtained under a very ::: restrictive “worst-case” hypothesis, where the attacker is assumed to be able to fabricate a perfect replica of a biometric trait whose matching score distribu-tion is identical to the one of genuine traits. Thus, we argue and investigate the validity of “worst-case” hypothesis using large set of real spoof attacks and provide empirical evidence that “worst-case” scenario can not be representa- ::: ixtive of real spoof attacks: its suitability may depend on the specific biometric trait, the matching algorithm, and the techniques used to counterfeit the spoofed traits. Then, we propose a security evaluation methodology of biometric systems against spoof attacks that can be used in real applications, as it does not require fabricating fake biometric traits, it allows the designer to take into account the different possible qualities of fake traits used by different attackers, and it exploits only information on genuine and impostor samples which is col- ::: lected for the training of a biometric system. Our methodology evaluates the performances under a simulated spoof attack using model of the fake score distribution that takes into account explicitly different degrees of the quality of fake biometric traits. In particular, we propose two models of the match score distribution of fake traits that take into account all different factors which can affect the match score distribution of fake traits like the particular spoofed biometric, the sensor, the algorithm for matching score computation, the technique used to construct fake biometrics, and the skills of the attacker. All these factors are summarized in a single parameter, that we call “attack strength”. Further, we propose extension of our security evaluation method to rank several biometric ::: score fusion rules according to their relative robustness against spoof attacks. This method allows the designer to choose the most robust rule according to the method prediction. We then present empirical analysis, using data sets of face and fingerprints including real spoofed traits, to show that our proposed models provide a good approximation of fake traits’ score distribution and our ::: method thus providing an adequate estimation of the security1 of biometric systems against spoof attacks. We also use our method to show how to evaluate the security of different multimodal systems on publicly available benchmark ::: data sets without spoof attacks. Our experimental results show that robustness of multimodal biometric systems to spoof attacks strongly depends on the particular matching algorithm, the score fusion rule, and the attack strength of fake traits. We eventually present evidence, considering a multimodal system based on face and fingerprint biometrics, that the proposed methodology to rank score ::: fusion rules is capable of providing correct ranking of score fusion rules under spoof attacks. <s> BIB009 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> II. BIOMETRIC ANTI-SPOOFING <s> The theoretical framework and algorithms of a novel method for the generation of synthetic on-line signatures are presented. This model-based approach combines the spectral analysis of real signatures with the Kinematic Theory of rapid human movements in order to generate totally synthetic specimens. Two different algorithms are also described in order to produce duplicated samples from the synthetic master signatures, so that the generation scheme as a whole is able to produce in a complete automatic fashion huge synthetic databases. Typical examples of synthetic specimens are presented to highlight their human-like appearance. The validation protocol and the test results are presented and discussed in a companion paper. <s> BIB010
In spite of some ongoing efforts and proposals to reach a unified and standardized nomenclature for vulnerability related concepts, the biometric community has still not reached a general agreement on the best terminology to be used in each case  ,  ,  . In light of the absence of a closed definition, the present article will follow the specialised literature where biometric spoofing is widely understood as the ability to fool a biometric system into recognizing an illegitimate user as a genuine one by means of presenting to the sensor a synthetic forged version (i.e., artefact) of the original biometric trait. Such attacks, also referred to in some cases as direct attacks BIB004 fall within the larger category ''presentation attacks'', defined in the latest draft of the ISO/IEC 30107 standard as ''presentation of an artefact or human characteristic to the biometric capture subsystem in a fashion that could interfere with the intended policy of the biometric system''  . Such a wider group of attacks also includes the presentation to the acquisition device of human characteristics (and not only synthetic artefacts) such as dead fingers, mutilated traits, real living traits under coercion or a different living trait (i.e., zeroeffort impostor attempts that try to take advantage of the False Acceptance Rate, FAR, of biometric systems)  . Therefore, spoofing consists in using an artificial trait to impersonate a different user or to create a new genuine identity. Several scenarios are typically conceived for spoofing attacks depending on the type of biometric system considered. (i) Verification system: In the most common case, spoofing is carried out at the time of authentication by presenting to the sensor a fake physical copy of the genuine's user trait. Such artefact is acquired and matched to the enrolled real template of the genuine user. (ii) Verification system/Identification system in closed set: Spoofing may also be performed at the enrolment stage by generating a new identity with an artefact (not necessarily imitating any real user's trait) which can later be used by different users to access the system. (iii) Identification system in open set: Typically this case corresponds to look-up systems where a new identity is created using the spoofing artefact to avoid being found in a watch list (e.g., to obtain a VISA for illegally entering a country). Given the above spoofing definition, an anti-spoofing method is usually accepted to be any technique that is able to automatically distinguish between real biometric traits presented to the sensor and synthetically produced artefacts containing a biometric trait. As in the spoofing case, although it is a very extended one, this nomenclature is not carved in stone and, very often, anti-spoofing approaches are also referred to in the literature by the terms liveness detection or vitality detection techniques. Rigorously speaking, both terms (i.e., anti-spoofing and liveness detection) are not fully equivalent, as not all anti-spoofing methods are necessarily based on cues directly related to living features of biometric traits. However, in practice, they are used as synonyms in the majority of cases. Therefore, in the present article we will not FIGURE 2. General diagram of a biometric system specifying the modules where the three types of anti-spoofing techniques may be integrated (sensor-level, feature-level and score-level). Also displayed are the two different type of attacks for which anti-spoofing techniques may offer protection: spoofing and attacks carried out with synthetic or reconstructed samples. make any difference between them. It is also worth noting that certain anti-spoofing techniques may also be highly effective to detect other types of presentation attacks (e.g., dead or mutilated traits). Anti-spoofing methods represent a challenging engineering problem as they have to satisfy certain demanding requirements BIB001 : (i) non-invasive: these techniques should in no case be harmful or require an excessive contact with the user; (ii) user friendly: users should not be reluctant to interact with them; (iii) fast: results should be generated in a reduced lapse of time as users' interaction with the sensor should be kept as short as possible; (iv) low cost: wide use cannot be expected if the cost is excessively high; (v) performance: in addition to a good fake detection rate, the protection scheme should not degrade the recognition performance of the biometric system (e.g., false rejection). From a general perspective, anti-spoofing techniques may be classified into one of three groups depending on the biometric system module in which they are integrated (see Fig. 2 ): • Sensor-Level Techniques. Usually referred to in the literature by the term hardware-based techniques. These methods add some specific device to the sensor in order to detect particular properties of a living trait (e.g., facial thermogram, blood pressure, fingerprint sweat, or specific reflection properties of the eye). As shown in Fig. 2 , such techniques are integrated in the biometric sensor. In general, hardware-based approaches measure one of three characteristics, namely: (i) intrinsic properties of a living body, including physical properties (e.g., density or elasticity), electrical properties (e.g., capacitance, resistance or permittivity), spectral properties (e.g., reflectance and absorbance at given wavelengths) or even visual properties (e.g., colour and opacity); (ii) involuntary signals of a living body which can be attributed to the nervous system. Good examples are the pulse, blood pressure, perspiration, pupillary unrest (hippus), brain wave signals (EEG) or electric heart signals; (iii) responses to external stimuli, also known as challenge-response methods, which require the user cooperation as they are based on detecting voluntary (behavioural) or involuntary (reflex reactions) responses to an external signal. Examples of such methods can be the pupil contraction after a lighting event (reflex), or the head movement following a random path determined by the system (behavioural). In the present work multibiometric techniques are included in this category BIB005 , BIB002 ,  , although, in some cases, they could reasonably be classified as well in the feature-level methods (described next). Multibiometric anti-spoofing is based on the hypothesis that the combination of different biometrics will increase the robustness to direct attacks, as, in theory, generating several fake traits is presumed to be more difficult than an individual trait. Following this assumption, multimodal approaches fuse different modalities. Generally, the strategy followed is to use complementary traits in terms of performance and vulnerabilities. Accordingly, very accurate traits vulnerable to spoofing (e.g., fingerprints) are combined with traits robust to spoofing with low recognition rates (e.g., the finger vein pattern). Such a strategy requires additional hardware acquisition devices, therefore these techniques may be included in the sensor-level group of anti-spoofing methods. Note that the above hypothesis (i.e., circumventing a multibiometric system implies breaking all unimodal modules) has already been shown to be untrue as, in many cases, bypassing just one of the unimodal subsystems is enough to gain access to the complete application BIB008 , BIB006 - BIB007 . Therefore, multibiometry by itself does not necessarily guarantee a higher level of protection against spoofing attacks. As such, specific protection schemes for multibiometric systems have started to be recently studied BIB009 ,  . • Feature-Level Techniques. Usually referred to in the literature by the term software-based techniques. In this case the fake trait is detected once the sample has been acquired with a standard sensor. As such, features used to distinguish between real and fake traits are extracted from the biometric sample (usually images, as in the case of face, or some kind of time-functions, as in the case of speech), and not directly from the human body as in the case of sensor-level techniques. These methods are integrated after the sensor, usually functioning as part of the feature extractor module (as shown in Fig. 2 ). They can be further classified into static and dynamic anti-spoofing methods, depending on whether they work with only one instance of the biometric trait, or with a sequence of samples captured over time BIB003 . Although they may present some degradation in performance, in general, static features are preferable over dynamic techniques as they usually require less cooperation from the user, which makes them faster and less intrusive. Such a subdivision into static and dynamic approaches is of special interest in face recognition, where there exist systems working on single facial images (e.g., passport picture) and on video sequences (e.g., surveillance camera). Although, for clarity, multimodality will be considered in the article as a sensor-level type of anti-spoofing countermeasure, some of these approaches can also be included in the present group. For instance, from just one single high resolution image of a face we may perform both face and iris recognition. In this particular case, a multimodal strategy is being applied at the feature extractor level, with no need of any additional hardware or sensing device. An appealing characteristic of software-based techniques is that, as they operate directly on the acquired sample (and not on the biometric trait itself), they are potentially capable of detecting other types of illegal break-in attempts not necessarily classified as spoofing attacks. For instance, feature-level methods can protect the system against the injection of reconstructed or synthetic samples 2 into the communication channel between the sensor and the feature extractor as depicted in Fig. 2 [91]- BIB010 . • Score-Level Techniques. Recently, a third group of protection methods which falls out of the traditional two-type classification (software-and hardware-based), has started to be analyzed in the field of fingerprint anti-spoofing. These protection techniques, much less common than the previous two categories, are focused on the study of biometric systems at score-level in order to propose fusion strategies that increase their resistance against spoofing attempts. Due to their limited performance, they are designed as supplementary measures to the sensor-level and feature-level techniques presented above, and are usually integrated in the matcher (as shown in Fig. 2 ). The scores to be combined may come from: i) two or more unimodal biometric modules; ii) unimodal biometric modules and anti-spoofing techniques; or iii) only results from anti-spoofing modules. A graphical diagram of the categorization proposed above is given in Fig. 3 . Although the present article will follow this three-group taxonomy, this is not a closed classification and some techniques may fall into one or more of these groups (e.g., as already mentioned, some multibiometric methods could be a good border-line example). Nonetheless, we believe that this taxonomy can help to get a clearer picture of the current biometric anti-spoofing scene. As well, the reader should be aware that, even though this is a quite extended and accepted classification, others are also possible. It is also worth highlighting that the three types of antispoofing approaches presented here are not exclusive, and may be coupled in order to improve the overall security of the system. In fact, the two most deployed methods described above (hardware-and software-based), have certain Fig. 3 ) according to their general compliance with the requirements defined in BIB001 . The last column shows the potential ability of anti-spoofing techniques to detect eventual non-spoofing attacks such as the ones carried out with synthetic or reconstructed samples as shown in Fig. 2. advantages and drawbacks so that, in general, a combination of both would be the most desirable protection strategy to increase the security of biometric systems. As a coarse comparison, sensor-level schemes usually present a higher fake detection rate, while feature-level techniques are in general less expensive (as no extra device is needed), less intrusive and more user-friendly since their implementation is transparent to the user. As already mentioned, score-level protection techniques present a much lower performance and are designed only to support sensor-or feature-level protection measures. As general reference, Table 1 presents a comparative summary, for the three classes considered in the article, of some of the most relevant characteristics that are desirable in antispoofing protection schemes BIB001 . The table should be understood only as a very broad indication of their capabilities. Therefore, in practice, a specific individual study should be carried out for each proposed anti-spoofing algorithm in order to determine its level of compliance with each of these features. In the following, as a concrete and very representative example of the research carried out in the field of spoofing, we present a comprehensive review of the most successful and popular anti-spoofing methods which have been proposed in the literature for the face biometric.