Spoofing detection on adaptive authentication System‐A survey

IET BiometricsEarly View REVIEWOpen Access Spoofing detection on adaptive authentication System-A survey Hind Baaqeel, Corresponding Author Hind Baaqeel 2190500152@iau.edu.sa orcid.org/0000-0002-6343-6114 Computer Science Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia Correspondence Hind Baaqeel, Computer Science Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia. Email: 2190500152@iau.edu.saSearch for more papers by this authorSunday Olatunji, Sunday Olatunji Computer Science Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia SAUDI ARAMCO Cybersecurity Chair, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaSearch for more papers by this author Hind Baaqeel, Corresponding Author Hind Baaqeel 2190500152@iau.edu.sa orcid.org/0000-0002-6343-6114 Computer Science Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia Correspondence Hind Baaqeel, Computer Science Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia. Email: 2190500152@iau.edu.saSearch for more papers by this authorSunday Olatunji, Sunday Olatunji Computer Science Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia SAUDI ARAMCO Cybersecurity Chair, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi ArabiaSearch for more papers by this author First published: 30 December 2021 https://doi.org/10.1049/bme2.12060AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract With the widespread of computing and mobile devices, authentication using biometrics has received greater attention. Although biometric systems usually provide efficient solutions, the recognition performance tends to be affected over time due to changing conditions and the ageing of biometric data, which results in intra-class variability. This issue is one of the leading causes of the high false rejection rate in biometric authentication systems. Fortunately, this issue has been addressed by using adaptive biometric solutions in which the system gradually adapts to new changes in user biometrics. However, their adaptability to changes may be exploited by an attacker to compromise the stored templates, either to impersonate a specific client or to deny access to him/her. In this work, the authors will carry out a systematic literature review by conducting a comparative study on state-of-the-art solutions for spoofing detection on adaptive authentication systems. This paper will identify the main issues that need to be addressed in adaptive authentication systems. Thus, the authors aim to encourage researchers to develop more robust adaptive solutions to overcome the identified gaps in this research. 1 INTRODUCTION With the new era of technology, the amount of information and its application is expanding at a fast rate. As a result, the need to protect information by correctly identifying legitimate users had getting higher more than ever. User Identification can be found in a wide range of applications including computer security, forensics, employee access, border control and financial transactions. Recently, biometrics has gained much popularity and is widely used as a secure way to identify an individual by distinguishing their unique biological and behavioural features [1]. Since there are usually very few chances that two individual shares the same biometric feature such as fingerprint or face feature [2], they have been used as user ID in which they can use to gain access to services. New development of Biometric systems has successfully provided an accurate, reliable, cost-effective way of authentication. They are believed to mitigate some of the limitations of traditional authentication methods such as passwords. For example, biometrics eliminated the need for memorisation. Also, they cannot be shared, and they are invulnerable to guessing attacks. On the other side, even though biometrics have been represented as the ultimate in authentication against traditional authentication methods, it is still prone to a particular limitation that is rarely found on other traditional methods [3]. Such as, the acquisition of user biometrics brings the problem of intra-class variation. This problem arises when the biometric features that have been taken on enrolment differ from the ones represented in the authentication phase. This problem raises the false rejection rate (FRR) in which legitimate users are denied access because the biometric system fails to match represented authentication information to the previously stored one. False matches can be used in a hill-climbing attack on the system and steal identity by trying out different composition until the correct formation replicate the user biometric. A high false match rate can be due to different reasons. For instance, changing environments can affect the performance of a biometric system because, during enrolment, the capture device cannot save all the possible conditions that the system can encounter during the matching phase. Another main reason for high FRR is ageing, which can affect both biological and physiological biometric features [4]. The performance of biometric traits usually degrades over time because of the reasons mentioned above; thus, they require re-enrolling the biometric traits on a regular time interval to overcome this limitation. However, the need to re-enrol is both time and cost consuming and hard to accomplish on a large-scale system. For this reason, the biometric system needs to learn how to adapt in response to different changes in the user biometric traits. The concept of adaptive authentication systems has gained much popularity recently. It refers to the system that adapts to new changes in user's authentication data. In such a system, there is no need to gather a large number of users' features during enrolment because the system can adapt to inclusion gradually [5]. Furthermore, it reduces the time and cost of re-enrolment and increases user convenience. It has two main types: supervised or unsupervised adaption. On the first type, the labelling process is done by a human (supervisor) wherein the other type of the labelling process is inferred without explicit labels. However, the adaptive authentication system still has open issues that need to be addressed, such as the need to define a clear adaption criterion and a stopping condition to prevent spoofing attacks. The adaptive authentication system can be targeted by attackers to gain unauthorised access or to block the recognition system from identifying them. In this case, the attacker tries to target mainly the adaptive features in order to manipulate the system to adapt to false user data. This type of attack is called a poisoning attack. It is considered the main vulnerability of adaptive authentication systems [6]. To demonstrate this further, in the case of adaptive face recognition systems, attackers try to poison the system by gradually changing the only little amount of the user's face to fool the system to adapt to these changes. These steps are gradually implemented until the system is fully poisoned and adapted to the new false user features, as shown in Figure 1. FIGURE 1Open in figure viewerPowerPoint Poisoning attack on adaptive authentication systems (Adapted from Ref. [7]) This paper presents a comprehensive survey about recent solutions to defend adaptive authentication systems against spoofing attacks. Section 2 will discuss the background of the current literature. Section 3 will provide the literature review. Section 5 will discuss and analyses the current state of art solutions. Section 6 will conclude the paper. 2 BACKGROUND 2.1 Biometric authentication Nowadays, knowledge-based authentication methods, such as password, have been replaced by biometric authentication [8]. This turnover happened because biometric authentication presented a more convenient and secure way to identify and recognise an individual since it is linked to one and only one user. Additionally, Biometric authentication overcomes some of the limitations that exist in knowledge-based systems such as forgetfulness, loss or guessing. In this type of authentication, a user biometric (either psychological or behavioural) is captured and translated into a digital template that is used later to identify the user or recognise him/her. It is a powerful security tool that proved its effectiveness in many authentication fields such as attendance, crimes forensics, e-commerce, financial transactions, border control and many others. The structure of a typical biometric authentication system is as follows: Enrolment phase: it is the first step of user authentication in which a special device is used to acquire the user biometric features Xn. Since the capturing phase is usually prone to noise η, the captured biometric feature usually goes through a quality check to make sure that it is reliable enough to be used later for verification. Then, these features along with an identifier form a user template M and are saved in a secured database. Verification phase: it is the second step of authentication in which the user presents his/her biometric feature to the same type of sensor used in enrolment. Then, a matcher processor tries to find a match between the entered template and the saved ones on the database. The matcher calculates a similarity score S with a certain threshold. If the similarity score exceeds the threshold, the user is granted access or otherwise denied access. If the object of the verification is to prove a claimed identity, then the presented template is needed to be compared only against the stored claimed identity (One-to-One). If the reason for verification is to identify a person. Then, the matching system needs to compare the entered template against multiple stored templates (One-to-Many). 2.2 Adaptive strategies for biometric authentication In an adaptive authentication system, the adapting unit uses information gained from user queries along with the biometric recognition unit. The adapting strategy is composed of five main aspects: Adaption Mode: adaptive systems can operate on two modes: supervised and semi-supervised. On the first mode, the system automatically adapts to references from unlabelled user samples. This adaption mode is usually preferred in an adaptive system since users' queries are usually unlabelled. The second mode is a supervised adaptive system in which the adaption process depends on true labels. Adaption Criterion: The decision of updating user samples mainly depend on surpassing adaption criteria. For example, the authentication system can use a double threshold (classification decision threshold + adaptation threshold) for qualifying samples into the update process [9]. The authentication system Researchers have suggested many specifications for adaption criteria such as Quality index [10], mixed criteria [11], Score normalisation [12], Query Acceptance [13] and many other specifications as discussed with more details in Ref. [14]. Reference Modelling: this aspect specifies the needed resources for the adaption process. Adaption can be done using one good quality capture or can require multiple samples. In general, there are three types of reference models: single sample, multiple samples or categorical/set of samples model. This choice defers from one adaptive system to others based on the type of authentication biometric. For example, a single reference model can work just fine on face biometrics but on a voice recognition system; it will not be enough to capture enough voice variability from one sample. Adaptation Periodicity: This aspect defines the period in which updating takes place. The adaption periodicity can be set offline, such as waiting for a certain number of samples to be collected or a specific period. The other choice is an online update or real-time update where the update takes place whenever a query surpasses adaptation criteria. However, this type of adaption periodicity leaves an open window for attackers by trying out samples that exceed the adaptation threshold until the system accepts a false user or is unable to identify a user. Adaption mechanism: This aspect involves specifying 'How' the adaptation takes place during the authentication process. When building an adaptive model, one should consider how the old samples will be handled after being updated. Some models use additives in which the newly updated sample are simply added to the old ones. This combination performs as a gallery for the reference model. On the other hand, to avoid accumulating unwanted, outdated samples, a model can replace the old samples with the new updated ones. This type of adaption mechanism is more prone to poisoning attacks in which the attacker tries to replace old and genuine user templates with new fake user templates. Suppose the replaced samples were not saved in any place, the genuine user identity will be wiped away with no trace. Another adaption mechanism is to select specific samples to keep and discard unnecessary ones. 2.3 The need for adaption on user authentication Every single cell in the human body replaces itself over seven years. That means there is not even the smallest part of you now that was part of you seven years ago Steven Hall, The Raw Shark The reasons behind intra-class variation can be found as follows: Changes in Environment Condition: it is the variation in the condition that was present during enrolment but differ during verification. Those changes include but are not limited to: variation in illumination, humidity, noise, movement and position. Ageing effect: it the changes that happen on a biometric trait due to the difference between the time of enrolment and time of verification. This variation can affect biological or behavioural biometrics. It affects face, fingerprint, voice, movement, iris and hand geometry [15]. Time changes: physical objects are subject to physical changes to their modalities such as injuries, blackheads, acne and wrinkles. This type of change can affect the performance of biometric systems [16, 17]. Furthermore, users usually develop a new behavioural pattern over time, which can result in a mismatch when compared to stored user templates. Emotion effect: human's emotions, including anger, fear, and happiness, can affect behavioural biometrics, especially in the case of voice recognition [18]. Human-sensor Interaction: The interactions between the user's and the sensor differ from one user to another. This variation in interaction dynamics is one of the intra-class variability and can affect the recognition/verification process [19]. For example, when the user puts on makeup (a self-occlusion) or puts on face accessories such as glasses or piercing, which were not there during the enrolment phase. 2.4 Security challenges in adaptive authentication environments An attacker can exploit a vulnerability in adaptive biometric authentication devices through the following: Imposter Attacks: Fabricating a fake biometric trait to impersonate an enrolled client. They target the sensor. It can specifically poison the adaption process to deceive the system into accepting a fake template. For example, acquiring the user fingerprint data representation can guarantee to pass the access control during the authentication process. Insider Attack: The attacker is colluded with a system administrator or exercises coercion to escalate privileges. This type of attack is not acquiring much attention as much as outsider attacks since the legitimate users are usually considered trustworthy. Database Attack: Template theft, substitution, and deletion attacks target the template database if templates are not protected properly. Replay Attacks: Staged at interfaces between modules by replaying a stolen image of the biometric trait of the targeted client to the feature extractor or directly the corresponding feature values to the matcher. Hill-Climbing Attack: Reconstruct a template image by iteratively sending a bunch of slightly modified images to the feature extractor or their features to the matcher. Malware Attack: The algorithmic implementations of the software modules can be exploited to install malicious software. 3 RELATED WORK 3.1 Adaptive authentication solutions Adding the adaptive features into biometric authentication systems has been introduced by many researchers to overcome the high FRR due to time-variant or changes in environmental conditions. One example of biometric intra-class variation is the change of the user's voice or facial features due to ageing [20]. Moreover, ageing has proven to play a role in degrading the performance of fingerprint recognition, specifically after the age of 40-45 [21]. Z. Akhtar et al. [22] studied the performance of adaptive biometric authentication under the effect of facial ageing. They concluded that adapting to age is suitable for daily activities such as mobile interactions or ATM but not in forensics in which a high age gap can present. Signature recognition is another biometric trait that has proven the suffer degradation from ageing [23]. Some simulation techniques have been proposed to estimate the ageing effect over time [24]. They can help to train the system to adapt to new biometric changes, but, they are still highly prone to estimation errors and not reliable enough to authenticate legitimate users. Rattani et al. [25] have proposed a self-trained model using graph-based techniques that can learn to adapt on its own. Their system proved to be reliable enough to correctly match new user's biometric templates with the stored ones. Devi and Marimuthu [26] have proposed a self-updated multi-authentication model that adapts to plastic surgery changes in user's faces. The idea of a self-adapted system is fascinating, but does it improve biometrics authentication performance? The answer to this question has been investigated in Refs. [27, 28], and they conclude that a self-updated biometric system, indeed, gradually improves performance and the overall system robustness. On the other hand, Zhao et al. [29] suggested co-training the matcher unit using Linear Discriminant Analysis (LDA) and Local Binary Patterns (LBP) features. Their experiment results have proven that co-updated biometric models have achieved better accuracy than self-updated ones. Mostafa et al. [10] have proposed enhancing the co-trained version of adaptive biometric systems using quality measures in the fusion phase. Their model results showed a sufficient reduction in the FRR rate by 8.8%. However, in this kind of model, the quality index needs to be defined, but it varies from one modality to the other. P. Pisani et al. [12] suggested adding a score normalisation phase to achieve better performance. Also, they proved that adaptive systems that choose score normalisation for each user individually achieve better than those that apply uniform score normalisation. M. Dewan et al. [30] proposed Adaptive Appearance Model Tracker-based Face Recognition (AAMT-FR). They proved that these types of systems achieve better performance than single capture systems. The reason behind this is that they are much capable of gathering variance in user's biometric data. 3.2 Security of biometric templates The technology of the Biometrics system is spreading extremely fast, and it has become easily accessible and affordable. Nevertheless, its complete accessibility made it vulnerable to different types of attacks. Biometrics resemble the unique user identity. If they were compromised, it could lead to severe consequences [31, 32]. In case of a poisoning attack, if the attacker has access to the saved user templates, it can engineer its attack with perfect knowledge, and that will increase its success probability. The easiest way for the attacker to reach user biometrics is by attacking the templates database [33]. To secure biometrics, state-of-the-art technologies fall into two main categories (Table 1): Cryptosystem: Cryptosystem is one of the most known ways of securing any type of data, including biometrics. In this technology, user biometrics is encrypted using an encryption key (usually derived from a password). The resulting template can only be ciphered using the corresponding decryption key. Tarif et al. [34] proposed an encryption/hiding scheme to protect transmitted biometrics through a communication medium. Their method provides invisibility, security and robustness, which is crucial aspects of any security system. Liew et al. [35] suggested encrypting biometrics using chaotic mapping. However, Jakimoski & Kocarev [36] have proved that this type of cryptography is not always secure. In Ref. [32], they have recommended avoiding encryption in biometrics because performing decryption at each authentication attempt leads to revealing the original template. Moreover, C. Rathgeb and A. Uhl [37] have pointed out that the generated ciphertext generated by the biometrics key is not so hard to decipher because of its low entropy. Template transformation (Cancellable Biometrics): This technology works by transforming or distorting user images in a one-way direction [38]. In other words, the transformed image cannot be reverted to the original image. The strength of this technology is that it can preserve user privacy in which the only reference to the user is the transformed version of the template. In Ref. [38], a cancellable fusion-based recognition system was proposed. In their proposed model, deep user features are extracted using a CNN; then, it is transformed using bio-convolving techniques. Several researchers discussed salting as a template protection technique. In this method, auxiliary data or noise is added to the biometrics to hide its original form [39]. Sardar et al. [40] have suggested a hybrid system that performs Non-Invertible transformation (i.e. Cancellable Biometrics) on encrypted biometric templates. This idea has proven to achieve exceptional performance in terms of security. However, the complexity of the system needs more consideration since it requires multiple deciphering operations at each check. TABLE 1. Comparsion between techniques used for securing biometrics Cryptosystem Template transformation Description Encrypting user template using a public key and decrypting the template during authentication using a private key User templates are transformed using a password or a distortion algorithm Strength High performance Irreversibility & non-linkability Limitation Key management; reveals original template Security depends on passwords or an algorithm References [34, 35, 40, 41] [38, 39, 42-44] By analysing the benefits of both security techniques, it is safe to say that cancellable biometrics is the best choice for securing biometrics templates. The reasons for this recommendation over cryptosystems methods are: first, it reserves user privacy since the original template is not used in the authentication process. Secondly, it provides more security since it protects against most traditional attacks such as spoofing and brute force attacks [37]. Lastly, it provides revocability where several instances can be generated in case the user template was compromised [37]. 3.3 Spoofing attack detection of adaptive authentication systems Over the last years, spoofing attacks has presented a challenge to the security of authentication systems. Even though the integration of an adaptive authentication system has proved to provide a better performance, it raised new vulnerabilities to the authentication system. The investigation of B. Biggo et al. [45] has located two attack points (Fusion Unit and comparison Threshold), which control the adaption process and can raise vulnerabilities. Specifically, the adaption process can be targeted by poisoning attacks in which they exploit the adaptive mechanism to enforce the acceptance of imposter templates [46]. To address this issue, P. Pissani et al. [47] suggested adding imposter templates to the adaptive model database in order to train the model to identify imposter attempts correctly. However, errors in classification between legitimate users and imposters can poison the system to accept them [14]. In Ref. [48], a dual-stage adaptive model is proposed. The first stage classifies genuine user templates. Then, the second-stage filter out samples to only keep informative samples and discard noisy and redundant ones. This model has proven to provide a higher error reduction of 67% than other state-of-the-art solutions. However, as stated in this research, it is still prone to imposter attacks as any other solution. R. Giot et al. [49] proposed a protocol that can estimate the defence level of adaptive biometric systems against zero-effort attacks. Researchers in Ref. [50] have proposed using ensemble classifiers to increase system robustness against imposter attacks. However, adding multiple classifiers can increase system complexity, and thus, it might not be suitable for limited-resources platforms. Moreover, using ensemble classifiers will only make attack attempts harder but will not notify users of such attempts. Although multibiometric [11] can improve the robustness of a biometric system, this solution cannot account for genuine changes in biometric traits due to the lapse of time or ageing. Feature invariance and signal restoration schemes aim to render biometric features robust to noise. For instance, in face recognition, several techniques have been developed to normalise face images against lighting via illumination normalisation [12], against pose or expression change via affine transformation, and the presence of glasses by glass-removal algorithms. However, due to a large number of possible sources of variations, attempting to normalise against one factor at a time could introduce artefacts. This situation is most notable for extreme pose correction. However, more importantly, the procedure does not account for biometric trait change due to ageing and lifestyle-related changes. In Ref. [51], they studied four different deep learning models against image degradation, which are: AlexNet, VGG-Face, GoogLeNet, SqueezeNet. Their study showed that all DL-based models are subject to performance degradation quickly and significantly by blurry images. A summary of the state-of-the-art researches in adaptive biometrics is shown in Table 2. TABLE 2. Comparison of previous researches on adaptive biometric authentication Paper Ref No. Proposed model description Paper purpose Drawbacks [45] Overview of the security of adaptive authentication models Identify vulnerabilities - [48] Dual-staged adaptive models Enhance adaption process Prone to imposter attack [49] Protocol to test possible attacks Estimate defence level against imposter attack - [23] Ageing simulator A secure way to estimate change over time to add a new genuine user template Highly prone to estimation errors and not reliable enough to authenticate legitimate users [50] Ensemble classifiers Defence against imposter attack Increased complexity might not suitable for limited-resource systems [9] Double threshold Defence against imposter attack Can only capture little variability in user's templates [47] Training adaptive authentication systems using examples of imposter templates Defence against imposter attack Errors in classification can poison the system [11] Multi-model adaptive authentication Defence against imposter attack Reduce user convenient; increased cost [52] Cost-based classifier Reducing the cost of classification Doesn't consider time (ageing) effect on user template Nowadays, deep learning models have an edge over traditional machine learning models. Using deep learning, the model can learn complex patterns from a given input. It has proven to perform very well on image classification problems [53]. For this reason, many researchers suggested using it for spoofing detection since they outperform traditional classification algorithms, as seen in Table 3. This algorithm filters the input nodes through a set of hidden layers. This process repeats until it reaches the output nodes, which gives the required classification results. One of the advantages of using deep learning techniques in images classification is that it does not require a hand-crafted selection of features [54]. The reason for this is that the DL-based classification algorithm can understand complex image features and select the best discriminative features on its own. This ability is what makes deep learning techniques better than simple feature extraction methods in image classification [55]. In case of irrelevant data or imbalanced d