Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> The article describes and analyzes the international survey of fingerprint and laboratory personnel about fingerprint forgery, conducted in four countries during the years 1998 through 1999. The awareness and the attitude of the professionals to the problem of fingerprint forgery was tested. <s> BIB001 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> Nowadays biometrics is being used in many applications where security is required. This fact causes that new threatens have appeared and that the number of attempts to break biometric systems has increased. From all potential attacks, those involving damage or thefts to users are the most worrying. Most of them could be avoided if acquisition sensors would have suitable approaches for aliveness detection at the capture process. Many providers claim that their products support these methods but unfortunately it has been discovered that some products do not detect fake samples. In this paper a methodology based on Common Criteria is given to evaluate, in an independent way, whether biometric capture devices implement methods for fake samples detection, and till which extent such methods are effective. This methodology has been tested with sensors from different modalities. <s> BIB002 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> The use of multimodal biometric systems has been encouraged by the threat of spoofing, where an impostor fakes a biometric trait. The reason lies on the assumption that, an impostor must fake all the fused modalities to be accepted. Recent studies showed that there is a vulnerability of the existing fusion schemes in presence of attacks where only a subset of the fused modalities is spoofed. In this paper, we demonstrated that, by incorporating a liveness detection algorithm in the fusion scheme, the multimodal system results robust in presence of spoof attacks involving only a subset of the fused modalities. The experiments were carried out by analyzing different fusion rules on the Biosecure multimodal database. <s> BIB003 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> In Schwarz 1 an amino acid model for printing latent fingerprints to porous surfaces is introduced, motivated by the need ::: for reproducibility tests of different development techniques for forensic investigations. However, this technique also ::: enables the fabrication of artificial traces constituting a possible threat to security, motivating a need for research of ::: appropriate detection techniques. In this paper a new framework for modelling the properties of a generic fingerprint ::: examination process is introduced. Based on the framework, examination properties and detection properties are derived ::: by a subjective evaluation. We suggest a first formalisation of exemplary properties, which can be easily extended to fit ::: different needs. We present a first experimental setup limited to two printers and the Schwarz amino acid model using ::: absorbing and non-absorbing material with first results to show tendencies and underline the necessity for further ::: research. <s> BIB004 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> We discuss the problem of combining biometric match scores with liveness measure values in the context of fingerprint verification. Recent literature has focused on the development of methods to assess if an input fingerprint sample is a “live” entity or a “spoof” artefact. This is commonly done by generating a single-valued numerical entity referred to as the liveness measure value. However, the problem of combining this liveness value with match scores has not been rigorously investigated. The goal of this work is to design a framework in which a liveness detector is incorporated with a fingerprint matcher. We first design and analyze three different methods to combine match scores with liveness values. Next, we introduce a Bayesian Belief Network (BBN) scheme that models the relationship between match scores and liveness values. Experiments carried out on a publicly available database of the Fingerprint Liveness Detection Competition 2009 (LivDet09) show the effectiveness of assuming a certain degree of influence of liveness values on match scores. <s> BIB005 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> The problem of detecting face spoofing attacks (presentation attacks) has recently gained a well-deserved popularity. Mainly focusing on 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices, a significant portion of these studies ground their arguments on the flatness of the spoofing material in front of the sensor. In this paper, we inspect the spoofing potential of subject-specific 3D facial masks for 2D face recognition. Additionally, we analyze Local Binary Patterns based coun-termeasures using both color and depth data, obtained by Kinect. For this purpose, we introduce the 3D Mask Attack Database (3DMAD), the first publicly available 3D spoofing database, recorded with a low-cost depth camera. Extensive experiments on 3DMAD show that easily attainable facial masks can pose a serious threat to 2D face recognition systems and LBP is a powerful weapon to eliminate it. <s> BIB006 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> Besides the recognition task, today's biometric systems need to cope with additional problem: spoofing attacks. Up to date, academic research considers spoofing as a binary classification problem: systems are trained to discriminate between real accesses and attacks. However, spoofing counter-measures are not designated to operate stand-alone, but as a part of a recognition system they will protect. In this paper, we study techniques for decision- level and score-level fusion to integrate a recognition and anti-spoofing systems, using an open-source framework that handles the ternary classification problem (clients, impostors and attacks) transparently. By doing so, we are able to report the impact of different spoofing counter-measures, fusion techniques and thresholding on the overall performance of the final recognition system. For a specific use- case covering face verification, experiments show to what extent simple fusion improves the trustworthiness of the system when exposed to spoofing attacks. <s> BIB007 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> An increasing number of studies have reported that the quality of biometric samples has a significant impact on the performance of the system. However, to our best knowledge, these studies are limited to impersonation attempts from different subjects, i.e., zero-effort attack, and they do not take into account the possibility of spoof attack, also called non-zero effort attack. In order to thwart the spoof attack, one way is to assess the likelihood of a spoof attempt by using biometric liveness measures. Since both biometric sample quality and liveness measures are different, and possibly complementary, we propose an information fusion framework that combines them under both zero- and nonzero effort (spoof) attacks. We implemented this framework using three generative classifiers, namely, Gaussian Mixture Model, Gaussian Copula, and Quadratic Discriminant Analysis. Experimental results on LivDet 11 spoof fingerprint database demonstrate that the proposed framework can reduce the error rate of the baseline system by about 56%, under both types of attack. <s> BIB008 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> User authentication is an important step to protect information and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech equipments. This article assesses how well existing face anti-spoofing countermeasures can work in a more realistic condition. Experiments carried out with two freely available video databases (Replay Attack Database and CASIA Face Anti-Spoofing Database) show low generalization and possible database bias in the evaluated countermeasures. To generalize and deal with the diversity of attacks in a real world scenario we introduce two strategies that show promising results. <s> BIB009 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> Fingerprint traces are an important part of forensic investigations to identify potential perpetrators. With the possibility of printing traces for quality assurance purposes it is also possible to place malicious traces on crime scenes. In forensics examiners are already aware of multiple identical traces, e.g. produced by stamping fingerprints. The technique of printing fingerprints using artificial sweat allows to create different versions of the same fingerprint, similar to the residue from a finger, which is almost never 100 percent identical to another latent fingerprint. Hence, Kiltz et al. 1 introduce a first framework for the detection of such malicious traces in subjective evaluations based on dot patterns of amino acid. Hildebrandt et al. 2 introduce a first automated approach for the detection of printed fingerprints using high resolution scans from a chromatic white light sensor. However, the reported recognition accuracy is insufficient for forensic investigations. In this paper we propose an improved feature extraction for scans using a confocal microscope to reduce the overall analysis time and to increase the recognition accuracy. Our evaluation is based on 3000 printed and 3000 real fingerprints on the three surfaces hard disk platter, overhead foil and compact disk advancing the research from Hildebrandt et al. 2 Our goal is to benchmark the feature extraction and recognition of printed fingerprints for the three substrates as well as for the combination thereof. The results indicate a significant reduction of the necessary analysis time to less than one minute as well as an improved recognition rate of up to 99.7 percent for all samples on the three surfaces in comparison to the previously achieved 91.48 percent on two surfaces as reported in Hildebrandt et al. 2 <s> BIB010 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> V. SUMMARY AND DISCUSSION: LESSONS, FACTS AND CHALLENGES <s> To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. In this paper, we present a novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment. The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on publicly available data sets of fingerprint, iris, and 2D face, show that the proposed method is highly competitive compared with other state-of-the-art approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits. <s> BIB011
In ''Skyfall'', the latest movie in the James Bond saga, 007 is given a gun that only he can fire: It works by recognising his palmprint, rendering it useless when it falls into a baddy's hands. This is just another example of the multiple uses which have been given to biometrics in films, TV and books, in most cases assuming a zero-error and perfect security performance. However, as usually happens in terms of technical advances, reality is still some distance away from fiction:  As reviewed in the present work, a great amount of research has been carried out concerning the vulnerabilities of biometric systems to direct attacks and multiple techniques to secure them against this threat have been proposed. Moreover, different independent evaluations have shown that some of these protection techniques are able to achieve very competitive results when assessed in laboratory conditions. However, in spite of all these efforts, commercial products, even those developed by the most advanced technological companies, keep failing to withstand the challenges posed by hackers. How can this situation be explained? Like in most cases, a simple answer is not possible as a number of factors have contributed to reach the current status. On one hand, in all issues related to spoofing, the biometric community has followed from the beginning the security through transparency principle, first formulated in the field of cryptography and then generalized to all other security areas as ''the enemy knows your system''  . This principle states that the fewer and simpler the things one needs to keep secret in order to ensure the security of a given system, the easier it is to maintain that security. In other words, there is no point in trying to deny or cover the vulnerabilities of biometric systems to spoofing because, sooner or later, attackers will discover them and the consequences will be unpredictable  . Therefore, quoting the Biometric Working Group already in 2003: ''public exposure of countermeasures and vulnerabilities will lead to a more mature and responsible attitude from VOLUME 2, 2014 the biometrics community and promote the development of more secure systems in the future''  . Such a philosophy will undoubtedly pay off in the long-run, however, in the short term, each user becomes a potential and well-informed hacker every time a new biometric product is released, which leads to situations such as the one described for the iPhone 5S. On the other hand, although some efforts have been made in the frame of the Common Criteria [41] , BIB002 and there is on-going work to release the first ISO standard specifically focused on spoofing  , still no largely extended standard exists regarding the evaluation of biometric vulnerabilities. This has resulted in very sparse certifications of biometricbased security commercial products compared to other technologies with a long standardization trajectory such as smart cards or cryptography  -  . A clear and well defined certification methodology to assess biometric security would certainly help developers in designing more robust and reliable systems. Also, we should not forget that the ''worst case scenario'' is specially sensitive in spoofing. Let's take for instance the example of face. It is not rare to find that an antispoofing technique is extremely accurate detecting certain spoofs (e.g., printed images), but that consistently fails when it is confronted with a different type of fake artefacts (e.g., videos), referred to in some cases as ''golden fakes''. In a real case, if the attacker finds this vulnerability, and following the transparency principle it should be assumed that he will, the system will be compromised. In this worst case scenario, even if the anti-spoofing method, on average, has an extremely high performance, the reality is that it will be easily spoofed. The ''worst case scenario'' presented above for face can be extended to all biometric traits, and makes spoofing evaluation a very difficult matter. It is especially challenging to recreate real attacking conditions in a laboratory evaluation. Under controlled conditions, systems are tested against a restricted number of typical spoofing artefacts, as it is unfeasible to collect a database with all the different possibilities that may be found in the market. Furthermore, assessment databases are usually divided into a train and a test set in which both of them contain examples of the same spoofs, therefore, protection techniques can be specifically tuned on the train set to detect what they already know will be present in the test set. However, the real world represents an open set evaluation with no constraints on the spoofs used to attack. In this scenario, what can be expected from the performance of protection techniques against any type of spoof and not only those for which it has been trained? In addition to the afore mentioned issues, so far, biometric spoofing questions have been addressed mainly relying on empirical testing carried out by individual research groups to evaluate the performance of specific techniques, often with small-scale datasets acquired in an ad-hoc way. Although this learn-by-doing approach allows gaining some insight into the new spoofing problem, there is still not enough understanding of how to quantitatively address fundamental performance tradeoffs and limits, for no ''theory on spoofing'' has been established yet. In the current spoofing context described above, and bearing in mind all lessons learned in more than 10 years of spoofing research, some still open questions are: Where do we go from here? What are the future challenges to be faced in biometric spoofing? What are the issues which still need to be looked into and further explored? What new lines of research can be foreseen in this field? In order to answer these questions, it cannot be neglected that absolute security does not exist: given funding, willpower and the proper technology, every security system can be compromised. However, there are a number of steps that can be taken in order to continue improving the security offered by biometric systems against spoofing attacks, so that the effort and resources needed to break them, exceed the benefit gained by the attacker. Probably, at this stage, one of the most urgent needs is to define a clear methodology to assess the ''spoofability'' of systems. This is not a straight forward problem, as there are new variables involved when the spoofing dimension is introduced. Although it is not yet generally deployed, an evaluation protocol which is gaining popularity for the assessment of biometric spoofing, defines two possible working scenarios  , BIB006 , BIB007 , BIB003 , BIB008 : • Licit Scenario, considered in classic recognition evaluations, it only takes into account genuine access attempts and zero-effort impostor access attempts. In this scenario performance is typically reported in terms of the FRR (False Rejection Rate, number of genuine access attempts wrongly rejected) and the FAR (False Acceptance Rate, number of zero-effort impostor access attempts wrongly accepted). • Spoofing Scenario, in which access attempts are either genuine or spoofing attacks. Although for this case there is still no agreed method for reporting results, two metrics which have been proposed and are starting to be used are the FRR (defined as in the licit scenario) and the SFAR (Spoofing False Acceptance Rate, corresponding to the number of spoofing attacks wrongly accepted). All these three metrics (i.e., FRR, FAR and SFAR) should be strictly assessed before carrying out any further evaluation concerning liveness detection techniques. This way, the real threat posed by a spoofing database to a certain recognition system can be determined. When a countermeasure is introduced in the system, all three previous measures should be recomputed considering the anti-spoofing module, so that they can be compared to the case with no protection against direct attacks. As such, the real impact of the liveness detection technique on the system performance can be fully characterized. Currently, when it comes to the evaluation of a new antispoofing countermeasure, in most cases, its performance is measured as an independent module and reported in terms of its classification rates, usually referred to as FFR (False Fake Rate, number of real samples classified as fake) and FLR (False Living Rate, number of fake samples classified as real). This stand-alone assessment constitutes a first necessary step towards the evaluation of the countermeasure but, as specified above, it should be complemented with further analyses. In fact, a liveness detection algorithm will, in general, not operate on its own, but integrated in a recognition system. In this framework, to perform an exhaustive assessment, other questions which are very rarely answered in current state-ofthe-art works, should be addressed: What is the impact of the FFR and FLR on the system performance (i.e., FRR, FAR and SFAR)? This issue can be addressed to a great extent following the two-scenario (i.e., licit and spoofing) evaluation protocol defined above. Although the above methodology would already represent a big advance if it was widely adopted or implemented into a standard, it still opens some new interesting topics for research which have just started to be explored: What is the best way to combine matching and liveness-detection scores BIB007 , BIB005 ? What would be the impact of this fusion in multibiometric systems BIB003 ? Can the performance of a system under both operating scenarios (i.e., licit and spoofing) be reported with one single metric? How can the above two-scenario methodology be generalized to the real case where all three score classes (i.e., genuine, zero-effort and spoofing) are present at the same time? Although current preliminary efforts in the study of spoofing assessment principles are hugely valuable, there is still quite limited theoretical foundation in this field to be able to answer some of the fundamental questions listed above. Accordingly, the biometric community needs to confront the existing challenges in order to build a solid theoretical background that allows further developing this area. Another shortcoming of most current anti-spoofing techniques, that should be analysed in the near future, is their lack of interoperability across databases. To date, the competitions organised have shown that top-ranked algorithms are able to achieve an accuracy close to 100%, however, their performance drops significantly when the testing dataset is changed. An interesting lesson may be learned from these results: There exists no clearly superior anti-spoofing technique. Selecting one particular protection method depends on the nature of the attack scenarios and acquisition conditions. Therefore, it is important to find complementary countermeasures and study the best fusion approaches in order to develop liveness-detection techniques that succeed at achieving a high performance over different spoofing data BIB009 , BIB011 . In addition to the above mentioned research topics aligned with spoofing fundamental theory, practical studies should not be forgotten. As technology progresses, new hardware devices and signal processing techniques continue to emerge. It is important to keep track of this rapid technological progress since some of the advances can be the key to develop novel and efficient anti-spoofing techniques. For example, just a few years ago, 3D acquisition scanners were unsuitable for liveness detection purposes due to their cost, size and level of cooperation required from the user. However, nowadays, there exist sensors which provide accurate depth information, with the size of a regular webcam and almost for its same price. Should they become integrated in biometric readers, such sensors could become in the near future a definitive solution against 2D photo and video attacks to face-based systems. The anti-spoofing community should also consider engaging in new fundamental research regarding the biological dimension of biometric traits, in order to break with the current popular trend embraced by many of the latest research where some well known sets of features (e.g, LBP, LPQ, HOG or BSIF) are extracted from images in public databases and passed through a classifier. Although such a methodology is valid, in most cases it brings little new insight into the spoofing problem. A greater progress could potentially be obtained from new studies exploiting intrinsic biological differences between real and fake traits such as, for instance, the thermogram or the facial blood flow. The impact of spoofing should also be considered in other biometric-related fields such as forensics. Sir Arthur Conan Doyle already foresaw the possibility of fake fingerprint forensic evidence in one of his renowned Sherlock Holme's short stories over a century ago  . Since then, different works have tested the ability of forensic examiners to distinguish between real and fake latent fingerprints  , BIB001 . However, only recently have some interesting research works addressed the automatic detection of fake finger-marks deliberately left behind at crime scenes BIB004 - BIB010 . This is a research line that can gain a lot of strength and importance in the years to come. Another more philosophical question to be addressed in the field of spoofing, is the balance between security and convenience. It is undeniable that one of the most important motivations, if not the most important reason, for the deployment and development of biometrics, is its security dimension. We want to secure access to information and biometrics represents a good alternative: you are your own key. In this context, spoofing gains great relevance: if the system is spoofed, the information is compromised. Therefore, it is clear that spoofing analysis is a key issue in the development of biometrics. However, it is important to keep in mind the final product where a biometric system will be integrated and its ultimate purpose within that product, as security is just one side of the coin. The other side is convenience. For certain applications, from the end-user perspective, some risk of spoofability may be acceptable if, in return, he obtains a greater gain in convenience. Let's go back to the iPhone 5S example. At the end of the day, are we sure it was meant to be unspoofable (even by simple known methods)? Was it worth making it more secure at the cost of increasing its price and the inconvenience to the user (more false rejections)? If someone is ready to go through the tedious process of lifting a latent fingerprint and casting a fake finger just to ''unlock our phone'', wouldn't he also be ready to obtain our 4 digit PIN (perhaps just by looking over our shoulder) if no biometrics were integrated in the device? In fact, it is estimated that over 50% of users had not set any security mechanism to unlock their mobile phones before the introduction by apple of the convenient fingerprint reader. If such percentage is decreased with the use of biometrics, it may be argued that a convenient and spoofable system has increased the overall security of mobile phones. Yes, the iPhone 5S can be spoofed, but, in practice, how many of them will be spoofed? Wouldn't they be hacked all the same without biometrics? As a wrap up conclusion it may be stated that, although a great amount of work has been done in the field of spoofing detection and many advances have been reached, attacking methodologies have also evolved becoming more and more sophisticated. As a consequence, there are still big challenges to be faced in the protection against direct attacks, that will hopefully lead in the coming years to a new generation of more secure biometric systems. In the meanwhile, Mr Bond will still have to wait some time until he gets a gun that he can fully trust to be the only one who can fire it.