Biometric Antispoofing Methods: A Survey in Face Recognition <s> 4) FACE ANTI-SPOOFING: SCORE LEVEL APPROACHES <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> BIB001 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 4) FACE ANTI-SPOOFING: SCORE LEVEL APPROACHES <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> BIB002 </s> Biometric Antispoofing Methods: A Survey in Face Recognition <s> 4) FACE ANTI-SPOOFING: SCORE LEVEL APPROACHES <s> Face authentication is one of promising biometrics-based user authentication mechanisms that have been widely available in this era of mobile computing. With built-in camera capability on smart phones, tablets, and laptops, face authentication provides an attractive alternative of legacy passwords for its memory-less authentication process. Although it has inherent vulnerability against spoofing attacks, it is generally considered sufficiently secure as an authentication factor for common access protection. However, this belief becomes questionable since image sharing has been popular in online social networks (OSNs). A huge number of personal images are shared every day and accessible to potential adversaries. This OSN-based facial disclosure (OSNFD) creates a significant threat against face authentication. In this paper, we make the first attempt to quantitatively measure the threat of OSNFD. We examine real-world face-authentication systems designed for both smartphones, tablets, and laptops. Interestingly, our results find that the percentage of vulnerable images that can used for spoofing attacks is moderate, but the percentage of vulnerable users that are subject to spoofing attacks is high. The difference between systems designed for smartphones/tablets and laptops is also significant. In our user study, the average percentage of vulnerable users is 64% for laptop-based systems, and 93% for smartphone/tablet-based systems. This evidence suggests that face authentication may not be suitable to use as an authentication factor, as its confidentiality has been significantly compromised due to OSNFD. In order to understand more detailed characteristics of OSNFD, we further develop a risk estimation tool based on logistic regression to extract key attributes affecting the success rate of spoofing attacks. The OSN users can use this tool to calculate risk scores for their shared images so as to increase their awareness of OSNFD. <s> BIB003
Recently, some research has also been carried out in the field of score-level anti-spoofing strategies for 2D face recognition systems. In one of these first works, the authors study the impact of anti-spoofing measures on the performance of practical face recognition systems. To this end, they analyse different score fusion techniques for the protection and authentication modules under a three-case classification scenario: clients, impostors and spoofing attacks BIB001 . In addition, several fusion strategies for the combination of outputs from anti-spoofing modules have been analysed in order to reduce the performance gap that can be observed when the evaluation database is changed BIB002 . Following a similar trend, the combination of scores given by dynamic-based and static-based anti-spoofing approaches has also been considered in  and BIB003 , showing a very significant improvement with respect to the individual systems, even for simple classifiers. In order to give a general overview of the different methods studied so far in face anti-spoofing, Table 2 presents a summary of some illustrative works referenced in the present section. The table should be understood as a tool for quick reference and in no case as a strict comparative study, since results shown in the last column have been obtained on different databases. The objective of the table is to schematically show the most relevant characteristics (i.e., type of antispoofing system, type of features used, evaluation database, results, etc.) of several representative articles, in order to help readers to gain, at a glance, an overall perspective of the different approaches studied so far in the field of face antispoofing. For an exhaustive and complete list of all published articles in the area the reader should refer to the inline text in this Sect. III. For further details on anti-spoofing evaluation, large publicly available datasets and comparative results from the face anti-spoofing competitions organized so far, readers are referred to Sect. IV.