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Biometric Vulnerabilities

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A taxonomy of biometric system vulnerabilities and defences
Article in International Journal of Biometrics · January 2013
DOI: 10.1504/IJBM.2013.052964
2 authors:
Yogendra Narain Singh
Sanjay Kumar Singh
Institute of Engineering & Technology - Lucknow
Indian Institute of Technology (Banaras Hindu University) Varanasi
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Int. J. Biometrics, Vol. 5, No. 2, 2013
A taxonomy of biometric system vulnerabilities and
Yogendra Narain Singh*
Department of Computer Science and Engineering,
Institute of Engineering and Technology,
Gautam Buddh Technical University,
Lucknow – 226 021, India
E-mail: singhyn@gmail.com
*Corresponding author
Sanjay Kumar Singh
Department of Computer Engineering,
Indian Institute of Technology (BHU),
Varanasi – 221 005, India
E-mail: sks.cse@itbhu.ac.in
Abstract: The interest in biometric technology is received much attention in
the recent years. However, the security issue still persists the main challenge
for the reliable functioning of biometric-based authentication systems.
Much has been reported on the vulnerabilities of biometric systems that breach
the security and user privacy. We present a high-level classification of
biometric system vulnerabilities and discuss the defence techniques of these
vulnerabilities. We present a multidimensional threat environment of the
biometric systems that includes faults, failures and security attacks. A
framework of biometric security attacks on man-machine model is presented
and the system vulnerabilities are represented using Ishikawa’s diagram. The
provable defence techniques such as biometric vitality detection and biometric
template protection are critically evaluated, in particular, a classification of
current state-of-the-art of vitality detection techniques of commonly used
biometrics is proposed. Our main contributions include: (1) propose a
taxonomy of biometric system vulnerabilities; (2) present a framework of
biometric security attacks using man-machine model; (3) representation of
vulnerabilities using Ishikawa’s diagram; (4) an evaluation of defence
techniques of these vulnerabilities.
Keywords: biometrics; authentication; vulnerabilities; vitality measures;
template protection; security; defences.
Reference to this paper should be made as follows: Singh, Y.N. and
Singh, S.K. (2013) ‘A taxonomy of biometric system vulnerabilities and
defences’, Int. J. Biometrics, Vol. 5, No. 2, pp.137–159.
Biographical notes: Yogendra Narain Singh is an Associate Professor in the
Department of Computer Science and Engineering at Institute of Engineering
and Technology, Gautam Buddh Technical University, Lucknow, India.
He teaches courses on pattern recognition, soft computing and machine
intelligence. He authored two books: Discrete Mathematical Structures (2010),
Wiley, New Delhi and Mathematical Foundation of Computer Science (2005),
Copyright © 2013 Inderscience Enterprises Ltd.
Y.N. Singh and S.K. Singh
New Age International, New Delhi. He has published over 20 research articles
on information security, biometrics and soft computing.
Sanjay Kumar Singh is working as an Associate Professor with Department
of Computer Engineering at Indian Institute of Technology, Banaras
Hindu University IIT (BHU), Varanasi, India. He is a co-author of more than
50 technical publications. His research interests include image processing,
biometrics methods and human computer interaction.
Biometric systems are becoming popular for security and authentication in most of the IT
community, but preserving the systems from threats that breach their security is a
potential challenge. Commonly used biometric systems authenticate a person by
capturing his/her facial or iris images, scanning the fingerprints or recording the voice or
speech samples. Persons authentication using biometric is attractive because the
authenticate process principally based on those characteristics that are unique and
measurable, in addition those are something that cannot be easily stolen and shared to
others. A significant difference between the traditional identity management system and
the biometric-based authentication system lies on the fact of matching process, i.e.,
error-free matching or error-tolerance matching. Unlike to a traditional identity
management system (e.g., passwords, tokens or PINs) that results the authentication
request to a simple ‘yes’ (completely matched) or ‘no’ (non-matched) outcome, a
biometric security system results the authentication request to how much similar or
dissimilar the biometric query is to its counterpart stored in the database.
Although the biometrics are unique among individuals but their representation may
vary during measurements. The variations in the biometric sample can be resulted due to
the acquisition environment or users interaction to the acquisition device. That yields the
inter-users similarity or intra-users dissimilarity. In order to fix the variability level of the
biometric data such that not to reject many authorised users or not to accept many
unauthorised users may open the space to intruders to making the system vulnerable and
circumvent its security. Instead, the framework of a biometric system that includes data
acquisition, processing, storing of templates and matching can also be threaten by an
adversary that result the problems of authentication accuracy, reliability, robustness
against fraudulent attacks, secrecy of biometric data and privacy protection.
A practical biometric system which is employed to different applications can perform
better and achieve the desired accuracy, but it is highly vulnerable to simple methods that
can circumvent the security (Dunstone and Poulton, 2011). The methods includes the
synthetic reproduction of anatomical identities, e.g., acquisition of facial images or lifting
of latent fingerprints; and the imitation of behavioural identities, e.g., reproduction of
handwritten signature or producing similar voices. Further, a replay of stored information
or false data can also be injected in the processing chain whereas the biometric features
extracted from the raw data can be copied as input to the biometric process and spoof the
system. The biometric system vulnerabilities that are resulted from spoof attacks are
shown in Figure 1.
A taxonomy of biometric system vulnerabilities and defences
Figure 1
A vulnerable biometric system resulting from spoof attacks (see online version
for colours)
Most often a practical biometric system suffers some degree of security threats, therefore
the likelihood of success to make the system vulnerability resistance depends on
analysing what kind of attacks it may be faced and what are their nature. Maltoni et al.
(2009) have presented a typical threat framework of a fingerprint recognition system. It
includes the following threat vectors: denial-of-service (DoS) that restricts the access
right of the privileged users. Circumvention refers the misleading of access rights and
gaining access by the unauthorised users. Repudiation includes the threats where a
malicious user deliberately denies having accessed the system. Collusion and coercion
threats refer the situation where an attacker is being helped by the privileged user like an
administrator and the legitimate user are forced to help the attackers, respectively.
Roberts (2007) has reported the attack vectors of a biometric system in the context of a
risk-based approach. Most of the studies including the cited ones have presented the
attacks concerning to spoof approaches and a limited work has been found to biometric
system faults and failures. We present a comprehensive look on the biometric system
vulnerabilities including the faults, failures and security attacks.
In this paper we present a high-level categorisation of security threats of a biometric
system and discuss the provable defences of these security threats. We present a
taxonomy of the biometric system vulnerabilities in a holistic and systematic manner. We
discuss the threat vectors of a biometric system in the context of faults, failures and
security attacks. We present a multidimensional threat environment of a biometric system
and representing their effects using Ishikawa’s diagram. As a countermeasure of
biometric system vulnerabilities, different techniques have been proposed in the literature
that is for the need of a reliable vitality testing and secrecy of the biometric data. We
critically evaluate each of these defence techniques and discuss their effectiveness in
protecting the biometric system from threats and preserving individual’s privacy. In
particular, a classification of the current state-of-the-art of the vitality detection
techniques of commonly used biometrics such as, fingerprint, face and iris is given. We
examine biometric template protection techniques such as template transformation and
biometric-cryptosystem used by different biometrics and estimate their performances on
the datasets and the test conditions that have used for the experiment.
Y.N. Singh and S.K. Singh
The rest of the paper is structured as follows: a taxonomy of biometric system
vulnerabilities are given in Section 2. To effectively guard against vulnerabilities, the
probable defences are described in Section 3. In Section 4, a discussion on the
effectiveness of different defence techniques is presented. Finally, the conclusions are
drawn at the end, in Section 5.
Biometric system vulnerabilities
The biometric security systems operate on different scenarios. It authenticates individuals
during the online (offline) access of the system like network (non-networked)
applications or within a security perimeter like inside home or office. Other scenarios
of these security systems are to authenticate individuals at physical entry with
non-repudiation like restricted area of airport or the remote access with non-repudiation
like web-based e-commerce applications. Depending upon the different scenarios,
various types of vulnerabilities occur in the biometric systems. The likelihood of
biometric system vulnerabilities, their nature and effects that breach the security of the
authentication process have been analysed (Maltoni et al., 2009; O’Gorman, 2003; Ratha
et al., 2001; Uludag and Jain, 2004; Jain et al., 2008; Roberts, 2007).
Mainly, the security of a biometric system can be breached at one or more levels of
its system design such as data acquisition, transmission channel or at the database where
biometric templates are stored. In general, vulnerabilities cause failure to a biometric
system primarily due to threats that can affect a system during its entire life. The life
cycle of a typical security system consists of a development phase and a use phase
(Laprie et al., 2004). The development phase of a biometric system includes all activities
from starting to their ends, e.g., pre-processing of acquired biometric sample, data
representation, feature extraction, and matching such that it has passed all tests
successfully and ready to use. During development phase, development faults may be
introduced into the system due to the system interaction with the development
environment (e.g., lacking competence or having malicious objectives of developers,
inadequate availability of development or testing tools, etc.). The use phase starts when
the system is ready to use and starts servicing to the users. During the use phase, a
biometric system interacts with its use environment (e.g., users, administrators and
intruders) and may be adversely affected by faults originating in it. During service
delivery, a service failure like incorrect or no service is delivered at the service interface.
An intentional shutdown of the service or a service without results are some of the other
common threats that need to be addressed.
At the highest level, the threats that result the vulnerabilities in a biometric system
can be classified as:
security attacks.
A tree representation of the threats of a biometric system that breaches its security is
shown in Figure 2.
A taxonomy of biometric system vulnerabilities and defences
Figure 2
A tree representation of security threats of a typical biometric system (see online
version for colours)
2.1 Biometric system faults
Biometric system faults include development faults (e.g., faults occurring during its
development phase), physical faults (e.g., faults that affect the hardware components) and
the external faults that are resulted from interaction with the use environment. The
examples of some developments faults are software aging (Grottke et al., 2008), data
corruption and storage space fragmentation (Bairavasundaram et al., 2008). There are
some faults that result from human actions such as the absence of actions when
the actions are required or performing wrong actions deliberately, these faults are
human-made faults. The objectives of human-made faults are: DoS, accessing of secure
information, improperly modify the system life cycle and disruption of services.
Depending upon the objectives of manager or users these faults may be malicious or
non-malicious. The objective of malicious faults is causing harm to system while the
non-malicious faults are resulted from the mistakes such as unintended actions of which
manager and user is not aware or deliberate faults that are resulted from wrong decisions.
Trojan horses, trap doors, logic bombs, viruses or worms are some of the examples of
malicious faults. Since the interaction faults occur during the use phase of a system,
therefore they are all operational faults such as wrong setting of system parameters that
may affect performance, storage, networking, security and privacy (Gray, 2001).
Y.N. Singh and S.K. Singh
2.2 Biometric system failures
Biometric system failures can be characterised as the deviation from implementation
of correct system functions. Generally, a system failure includes service failures,
development failures and security failures (Laprie et al., 2004). A service failure occurs
when the delivered service deviates from the correct service. The development failures
result from the development faults. A development failure causes the development
process to be terminated before the system is accepted for use and placed into service. It
can occur at any level of system design due to inefficient imaging, improper data
representation or improper matching. The development failures are primarily occur due to
unclear or misleading estimate of the complexity of the system to be developed. It
includes: inadequate design with respect to the functionality or performance goals, faulty
or incomplete specifications, inadequate fault removal capability and faulty estimates of
development costs. For example, the Unique Identification Authority of India (UIDAI)
programme of the Government of India aims to provide biometric-based unique
identification (UID) number to all its citizen is now struggling from the challenges. The
success of UID programme is questioned because the complexity (e.g., technical, social
or financial) of such a mega project have not been estimated in depth. The UID authority
always underestimated the complexity of this project (Singh, 2011). Therefore, we should
not surprise if an ambitious project like UIDAI fails to achieve their objectives. If it
happens, then the most severe part of this project failure would be the loss of money that
will cross 30 US$ billion which is more than the cost of AAS system that had been ended
due to complete development failure (US Department of Transportation, 1998).
A security failure occurs when a system suffers service failures more severely
than acceptable. It may be due to the setting of very high attributes such as efficiency,
reliability, integrity, confidentiality, safety and maintainability. The security failures are
the serious issues to a system when the probability of false accepts and the probability of
false rejects become high. The limited individuality of a biometric features also leading to
incorrect authentication and thus the systems are vulnerable to security failures. The
interclass similarity and intraclass dissimilarity of biometric features cause a failure to the
system due to fraudulent match and fraudulent non-match, respectively. In addition, the
presence of inherent noise and artefact at sensors can also lead to security failures.
2.3 Biometric security attacks
A biometric security system typically works in a man-machine model. Here machine
refers an auxiliary system comprising hardware and software components of a biometric
system including its infrastructural components. The man refers one or more person(s)
that are responsible for the proper functioning or supervising the system. An adversary
can attempt to harm a biometric system by targeting on the machine or targeting on the
man supervising the system in a number of ways. Ratha et al. (2001) have highlighted
different sources of adversarial attacks on a machine of a biometric system. We present a
modified framework of attack points in a man-machine model of a typical biometric
system as shown in Figure 3.
A taxonomy of biometric system vulnerabilities and defences
Figure 3
Modified framework of attack points in a man-machine model of a typical biometric
authentication system (see online version for colours)
Notes: The attacks type (1) are user level attacks, attacks type (2) to (6) and (8) to (9)
are on components and their interfaces while attacks on biometric templates are
depicted by type (7). Attacks of type (10) are on supervisory bench.
Martinez-Diaz et al. (2011) have classified the biometric system attacks into direct and
indirect attacks. Former refers the attacks of fake biometric samples with an aim to spoof
the sensor and trying to impersonate a real user. It is worth noting that the attackers
classified under direct attacks require any specific knowledge of the targeted biometric
system such as its development phase, e.g., data representation or matching. Indirect
attacks include the rest of types reported by Ratha et al. (2001) such as the attacks on
communication channel and the attacks on template database. In order to perform the
indirect attacks, attacker must know the specific information about the system such as the
communication protocol, template format or matching algorithm. Moreover, the attackers
need physical or logical access to internal parts of the system that is not available to the
We can classify the attacks on machine that are concerning to development phase and
use phase of a biometric system as:
user level attacks
attacks on components and their interfaces
attacks on biometric templates.
Y.N. Singh and S.K. Singh
2.3.1 User level attacks
The user level attacks occur mainly due to presenting of fake biometric samples to a
biometric system for identity verification. These attacks correspond to the attack type (1)
as shown in Figure 3. A fake biometric sample can be a fake finger made of gelatine, a
digital facial image or a replay of recorded voice. The commonly used biometrics, e.g.,
face, fingerprint, iris and voice are not secrets. People leave their physical prints of finger
on everything they touch, iris patterns can be observed anywhere they look, faces are
visible and voices are being recorded. The presence of biometric prints publicly, offer an
opportunity to intruders to lift these prints and copy them as real. The digital sample of a
biometric identity can be obtained covertly from the system and replay the forge sample
at the acquisition sensor. These attacks are common on the systems that do not have
sufficient security measure to distinguish between fake and genuine biometric prints and
thus deceived by intruders.
2.3.2 Attacks on components and their interfaces
The components of a biometric system that are highly susceptible to adversarial attacks
include quality checker (preprocessing), feature extractor, template database and matcher.
The examples of these attacks are the replay of raw biometric print or the injection of
false data in the processing chain of a system as depicted by attacks type (2) and (3),
respectively (see for instance Figure 3). At the feature extractor component the
biometric features extracted from the raw data can be overridden [attacks type (4)] or a
synthetically prepared feature vector can be injected as a test vector for matching
[attacks type (5)]. One approach that produces the synthesised templates is described as
hill-climbing (Adler, 2003). This technique works iteratively and improve the synthesised
features until it matches falsely to the stored template. While these attacks are taken on
the system a legitimate user neither noticed any exception nor presage from the system,
however it continues to provide them access.
Attacks on matcher to override the match scores in order to change an impostor’s
score to a higher passing score are avenues of attacks of type (6). The attacks type (7)
target the template database with an aim to add, modify or delete user information, we
will discuss these attacks separately. The attacks type (8) intercept the transmission
channel to control the flow of template information and override with tempered
information. Finally, attacks type (9) aim to override the matching decision that can result
acceptance to an impostor and rejection to a genuine user. The interfaces of different
components are attacked with an aim to hide the intermediate code of a component and
intercept the information reaching to the next component. For example, the code
generated at feature extractor can be intercepted by the malicious programs like Trojan
horses or logic bombs and a new (forge) set of features as desired by an adversary is
produced. Similarly, a matcher can be attacked by trap doors or viruses so that it bypasses
the matching process or it always produces the higher matching scores and thus
circumvent the system.
2.3.3 Attacks on biometric templates
Templates are the key samples of a biometric identity collected from the enrolled
population for their authentication. The biometric identity of an individual is not a digital
certificate that can be issued by a third party when the templates are stolen form the
A taxonomy of biometric system vulnerabilities and defences
database. For example, an iris-based recognition system authenticates individuals using
their iris codes. If someone steals the templates of iris codes then the only possibility left
to the users is to use the iris of other eye, nothing else. In case of voice recognition
system, if the voice print is stolen by an adversary then it remains stolen for whole life
and the user’s identity can never return to a secure situation. Different scenarios are
reported when the template of a legitimate user is attacked by an adversary. An adversary
can replace the genuine template from fake template that results an adversary got access
to the system. An adversary can modify or corrupt a genuine template that results a DoS
to a legitimate user.
In addition to non-secure infrastructure, a biometric system is also vulnerable to
various attacks that exploit the administrative loopholes. The administrative bench that is
responsible for efficient functioning of a biometric system can be targeted by an
adversary. We classify these attacks as the attacks on supervisory bench.
Figure 4
Ishikawa’s diagram for representing biometric system vulnerabilities (see online version
for colours)
Integrated System Failure
of biometric
of attributes
Attacks on
Supervisory Bench
Development Failures
External Faults
Physical Faults
of the user
Attacks on Components
and their Interfaces
Operational faults
Trojan horse
Inherent noise
and artifact
feature extraction
decision making
Inefficient sensing
Inefficient matching
Development Faults
Fake biometric
Attacks on
Biometric Templates
Attacks on
User Level
2.3.4 Attacks on supervisory bench
The attacks of type (10) are shown in Figure 3 target the administrative bench or the
group of persons that are responsible for safe and secure functioning of the system. If a
Y.N. Singh and S.K. Singh
system is functioning under inadequate administrative measures then it is vulnerable
and recumbent to adversarial attacks. For example, the users can be frightened and
forced by an adversary to provide their biometric samples. An authority can itself do
malfunctioning such as to modify the system parameters or to make them available to
adversaries for incursion by a hidden agreement between them. One of the harmful effect
of these attacks is being on the privacy of the individuals. While accessing the personal
data of individuals illegally, the adversary can disclose their privacy that may sickening
their personal and social life. Last but not the least, the administrative bench can change
the access rights of an individual in their interest. For example, when rights are curtailed
that may cause false rejects or DoS. When the rights are increased that may cause false
accepts. Alternatively, the security of the biometric system breaches in both ways.
The vulnerabilities of a biometric system discussed in this section can be represented
pictorially by Ishikawa’s (1986) diagram. Using this diagram, the multiple threat vectors
that can make the system vulnerable are represented and shown in Figure 4. It shows the
cause and effect of various security threats of a generic biometric system that leads to
integrated system failure.
Biometric system defences
Biometrics are unique among individuals but they are not secrets. Biometric information
is irrevocable and hard to regain identity (Watson, 2007). Therefore, the challenge is to
design a secure and robust authentication system from the system components that are
neither secrets nor revocable. A typical biometric system works by first storing the
features extracted from an enrolled biometric identity as templates in the system database
and then matching the template features with those extracted from the biometric
information presented during subsequent authentication attempts. A biometric security
system works perfectly if the system guaranteed that the biometric features are extracted
from a person to be authenticated and then it matches the template features in the
database (Schneier, 1999).
Ideally, no electronic authentication (eID) system is completely secured and no single
protection mechanism is sufficient to protect the system comprehensively. But the
sensible and practical measures can effectively reduce the risk of security threats to an
acceptable level. There are a number of proved defensive techniques in practice that are
effectively guard or reduce the risk of security threats and vulnerabilities of the biometric
systems. The security techniques of a generic biometric system that are effective against
system attacks can be grouped in two classes:
vitality detection
biometric template protection,
whereas each class has its own appropriate security mechanisms. Designing of salient
feature detectors and robust matchers are other effective countermeasures that can reduce
the faults and failures of a biometric system. In addition, practical approaches like use of
multiple biometrics, good governance practices and physical security can also effective in
reducing the security threats of the biometric systems.
A taxonomy of biometric system vulnerabilities and defences
3.1 Vitality detection
Vitality detection is a potential countermeasure against the spoof attacks of a biometric
system. It insures that the presented biometric sample is live not fake. In addition, it
insures that the presented biometric belongs to a live individual who was originally
enrolled in the system and not just any live person with or without fake biometric. The
objective of vitality detection is an actual measurement of biometric sample that is being
taken from a legitimate and live individual, who is indeed present at the time of
enrolment. The successful functionality of vitality detection techniques essentially
improve the reliability of a biometric system because it enables the system to reluctance
against artefact to be enrolled and ensuring that no non-live sample is accepted.
Although, biometric systems use individual’s physiological information for his/her
authentication, that hardly detects its vitality. It has however shown that the biometric
systems can be spoofed using fake samples, e.g., a fingerprint system can be spoofed by
an artificial finger prepared from gelatine, silicon, latex or Play-Doh (van der Putte and
Keuning, 2000; Matsumoto et al., 2002). The static and high resolution images of
face and irises can spoof the face recognition system (Schuckers, 2002; Adler, 2003;
Kollreider et al., 2005) and iris recognition system (Matsumoto, 2004, 2007),
Figure 5
Proposed classification of current state-of-the-art vitality detection techniques of
commonly used biometrics (see online version for colours)
In order to assure the vitality signs from biometric samples, different techniques have
been proposed in literature. Singh and Singh (2011) have proposed a classification of
current state-of-the-art vitality detection techniques of commonly used biometrics (e.g.,
fingerprint, face and iris) which is shown in Figure 5. The existing techniques can
broadly be divided into two classes:
hardware-based techniques
software-based techniques.
Hardware-based techniques detect the vitality signs from the available biometric sample
during the acquisition stage. These methods use an extra hardware to acquire the life
signs from presented biometric data. For example, the techniques used to measure the
vitality signs from fingertip placed on sensor include temperature (Kallo et al., 2001),
Y.N. Singh and S.K. Singh
odour (Baldissera et al., 2006), pulse oxiometry (Reddy et al., 2008), blood flow (Lapsley
et al., 1998) and spectral information (Coli et al., 2007). An integration of specific device
at the sensor increases the cost of the system while the additional circuitry could make it
invasive to the users.
Figure 6
(a) Fingerprints: real, silicon and gummy (Matsumoto et al., 2002) (b) Faces: fake and
live (Jee et al., 2006) (c) Irises: real and fake (Daugman, 1999) (see online version
for colours)
Note: All from left to right.
Software-based techniques detect vitality signs from biometric samples during processing
stage. The rationale behind those techniques are to extract any one peculiarity of live
signs from a single sample (static techniques) or from multiple samples (dynamic
techniques) that differ from artificial reproduction. In a fingerprint recognition
system, the vitality signs of a biometric sample can be detected by analysing a single
image of fingerprint using skin perspiration (Parthasaradhi et al., 2005), morphology
characteristics (Moon et al., 2005), spectrum analysis (Chang et al., 2011) and quality
related features (Galbally et al., 2012); or multiple images of a fingerprint using skin
distortion analysis (Antonelli et al., 2006). Similarly, a live sample of face or iris can be
distinguished from their fake images by analysing the Fourier spectrums (Li et al., 2004;
Daugman, 1999), statically. An image sequence of face is used to detect the live signs by
analysing the movement of eyes (Jee et al., 2006) and spatial 2D motions on the face
(Kollreider et al., 2005), dynamically. The image sequence of iris can detect the life signs
using pupillary movements and the triggering of pupils with illumination (Daugman,
3.1.1 Multimodal techniques of vitality detection
The measures of vitality detection can be enhanced by acquiring multimodal data for
identity verification. It has been shown that inclusion of more than one biometric
information complementing each other for robust authentication (Chetty and Wagner,
2004; Bredin and Chollet, 2007). Chetty and Wagner (2004) have proposed a system that
combines the face information with the voice information. The combined system of face
and voice can be able to verify the vitality of biometric samples through synchronisation
between movements of lips and the voice prints recorded in the system. Bredin and
Chollet (2007) have proposed a technique that fuses voice and visual biometrics after
their analysis at classification level. The two systems of face and voice recognition are
run independently and verify the correspondence between visemes and phonemes for
vitality detection. Singh et al. (2012) have proposed a multibiometric system that fuses
the face and the fingerprint biometrics with the electrocardiogram (ECG). The ECG has
suggested a vitality-enabled biometric (Singh and Gupta, 2009, 2011) that may provide a
good check against fake enrolments. The reported performance of the aforementioned
system is optimum and claimed to be robust against spoof attacks.
A taxonomy of biometric system vulnerabilities and defences
3.1.2 Performance evaluation of vitality detection techniques
Despite a variety of vitality detection techniques are known but the problem of assuring
vitality from biometric samples is practically harder (Toth, 2005). Independent measure
of vitality detection shows that the matching difference of distribution between live and
fake samples is smaller than the matching difference of distribution between genuine and
impostor samples. Therefore, spoofing the system in absence of vitality detection
technique causes a false match without doing the adversary effort. Moreover, the
performance of vitality detection techniques can be measured by computing the
proportion of transactions with a fake sample that are incorrectly matched (FMRNL) and
the proportion of transactions with a live sample that are incorrectly non-matched
(FNMRL). Equal error rate (ERR) between FNMRNL and FMRL can also be used for this
purpose. The testing results obtained from the methods associated with different
biometrics cited are shown in Table 1. However, it is harder to ascertain any conclusion
to the effectiveness of one vitality detection technique to another.
Table 1
Performance estimates of vitality detection techniques associated to different
Techniques and datasets used
Power spectrum (Coli et al., 2007) –
720 live and fake fingerprints (36 subjects).
EER: 0.6–6.3%
Perspiration (Parthasaradhi et al., 2005) –
33 Play-Doh fingerprints (33 subjects).
accuracy ~90%
Fingertip morphology (Moon et al., 2005) –
23 live and 34 fake fingertips.
Skin deformation (Antonelli et al., 2006) –
90 live and 40 fake fingerprints (45 subjects).
3D head movements (Kollreider et al., 2005) –
200 live and fake images.
Classification error:
Facial micro-movements (Jee et al., 2006) –
100 live and fake faces.
Multimodal Face and voice (Chetty and Wagner, 2004) –
19 female and 24 male (43 subjects).
Audio-visual sequences (Bredin and Chollet, 2007) –
624 synchronised and 14,352 unsynchronised
audiovisual sequences (26 subjects).
Face, fingerprint and ECG (Singh et al., 2012) (78 subjects).
FMRNL: 0.01%,
FNMRL: 0.08%
EER: 1–5.1%
EER: 0.2%
Notes: EER: equal error rate, FMRNL: false match rate of non-live sample,
FNMRL: false non-match rate of live sample and N/R: not reported.
3.2 Biometric template protection
To effectively guard against biometric system vulnerabilities, it is important to protect the
biometric templates stored in the database. Biometric templates are the key documents
used for establishing authenticity of the individuals. Therefore, templates are essentially
kept secure and protected from the reach of intruders. If the security of database
templates is breached then security of the system is compromised adversely.
Y.N. Singh and S.K. Singh
Ideally, a template protection scheme should satisfy a number of requirements (Jain
et al., 2008). First, the stored template should not exhibit the original sample that can be
replayed to the system. It should be computationally harder for an adversary to guess and
revoke the original sample or any close replica from the stored data. Secondly, template
database must be anonymous, i.e., the biometric data of an individual can be used as
multiple and varied identifiers for different applications without correlating with one
other. For example, if the biometric sample of an individual is compromised then a fresh
and new sample can be generated from the same biometric identity of that individual.
Finally, the template protection mechanism should not lead any significant degradation in
matching performance, i.e., increase of EER of the biometric system.
A number of hardware- and software-based techniques have been proposed to protect
the stored template present in the system (PlusID, Ratha et al., 2007; Wang and
Hatzinakos, 2009; Chin et al., 2006; Teoh and Chong, 2010; Bolle et al., 2002; Soutar
et al., 1998; Juels and Sudan, 2002; Hao and Chan, 2002; Clancy et al., 2003; Hao et al.,
Hardware-based techniques use smart cards or stand-alone biometric system-ondevices as shown in Figure 7(a). An example of such a solution is a commercial product
called privaris PlusID. The main limitations of the hardware-based techniques are that
they are expensive and inconvenient mainly because a user has to carry them always and
are prone to being lost. In the software-based techniques, the biometric features are
integrated with some external key and the resultant data is stored in the system database
instead of the original biometric template. The software-based template protection
techniques include feature transformation and biometric cryptosystems. Former technique
transforms the biometric features of an individual sample using a user specific key such
that the matching is being performed in the transformed domain. Latter technique
associates a cryptographic key with the biometric template of an individual to generate
biometrically-encrypted data which does not reveal any information about the original
template or the cryptographic key.
3.2.1 Template transformation
Consider F is a transformation function applied to a biometric template T which
generates its transformed information Fk(T), where k is a user’s specific key. Let Q be a
biometric query then using the transformation function F its transformed information
Fk(Q) is generated. Let M be a matching algorithm that performs matching between
biometric samples T and Q and returned a match score. A matching decision either accept
(1) or reject (0) can be taken on the basis of decision threshold λ, i.e.,
⎧accept, if M ( Fk (T ), Fk (Q) ) ≥ λ
Decision = ⎨
⎩reject, otherwise.
The schematic diagram of template transformation process is shown in Figure 7(b). The
choice of function F should be non-invertible. Because, the non-invertible transform is
strictly a one-way function. It means that for a given transformed information Fk(T) with
an user’s key k, the original template T should not be revoked in a reasonable amount of
time. Consequently, it is computationally harder for an adversary to invert a transformed
template to its original form even if the user’s key is compromised. The key issues of a
template transformation techniques are the selection of a transformation function that
A taxonomy of biometric system vulnerabilities and defences
conserved the discriminability of template or query data and maintained the secrecy of
user specific key utilised in the transformation process. Practically, it is harder to design
a transformation function that meets both the requirements of discriminability and
non-invertibility, simultaneously.
Figure 7
(a) The PlusID is a portable device with a built in fingerprint sensor (b) Schematic
diagram of template transformation technique used for template protection in biometric
security system (see online version for colours)
Note: Upon scanning a finger and matching it with the stored template, the device
wirelessly transmits a secure key that can be used for authentication
In the literature, different non-invertible transformation functions have been proposed for
different biometrics, i.e., fingerprint (Ratha et al., 2007), face (Wang and Hatzinakos,
2009), iris (Chin et al., 2006) speech (Teoh and Chong, 2010), etc. In general, the
suitability of a transformation function depends on the selected biometric, characteristics
of the feature set and the application area. The concept of cancellable biometric
perpetrated by Bolle et al. (2002) as a security enhancing technique to produce
anonymous biometric data is of great interest among biometric researchers. It protects the
biometric system from unauthorised tracking of the individuals and restricting the
possibility of cross-matching among different biometric databases, thus preserving an
individual’s privacy.
Ratha et al. (2007) have generated cancellable fingerprint templates using
non-invertible transforms. They have proposed Cartesian, polar and surface folding
transforms for minutiae data. Wang and Hatzinakos (2009) have addressed the problem
of changeable face and privacy preserving face recognition. They have proposed a
technique for generating cancellable faces using random projection in conjunction with a
sorted index numbers. A cancellable iris biometrics, coined as S-iris encoding has been
proposed by Chin et al. (2006). S-iris encoding combines iris feature and tokenised
Y.N. Singh and S.K. Singh
pseudo-random number via iterated inner product and render a set of cancellable bit
string. Teoh and Chong (2010) have presented a two factor cancellable formulation for
speech biometric using probabilistic random projection. The method offered the
protection of speech signal by hiding the actual speech feature through the random
subspace projection process.
The practical utilisation of biometric template security are reported in TURBINE
(http://www.turbine-project.eu) and UIDAI (http://www.uidai.gov.in) projects. The aim
of Trusted Revocable Biometric Identities (TURBINE) project is to commercialise eID
through fingerprint biometrics and enhanced privacy protection. The research interest of
the project is to do the identity verification in the transformed domain so that the data for
authentication cannot be used to restore the original biometric information. In addition,
anonymous data is to be created for different applications from an individual’s fingerprint
whilst ensuring that these identities cannot be linked to each other. In UIDAI project the
templates are secured using encryption-decryption criterion. The original biometric
images of fingerprints, irises and face are archived and stored offline while only the
encrypted information is stored on the server for verification purpose. Therefore, data
used by automatic biometric identification system is anonymised as claimed by the
3.2.2 Biometric cryptosystems
In the recent past, researchers have drawn their attention on the fusion of two most latent
security technologies, biometrics and cryptography. Biometrics is a security technology
used to authenticate individuals using their body mark with a high degree of assurance
while cryptography is used to assure the secrecy and authenticity of information in the
communication channel. A fusion of biometrics and cryptography is referred as biometric
cryptosystems (Soutar et al., 1998). A biometric cryptosystem associates a cryptographic
key (k) to an individual biometric template (T) and generates the biometrically-encrypted
data H = F(T, k). Biometrically-encrypted data is the helper data (H) that may not reveal
any relevant information about the template sample or the cryptographic key. While the
cryptographic key is being recovered at the time it matches to the query sample (Q), i.e.,
k = M(F(T, k), Q). The schematic diagram of biometric-cryptosystem process is shown in
Figure 8.
The key advantage of using biometric cryptosystem is that it stores the digital
signature of an individual in the database instead of storing the original biometric
template. Therefore, it creates anonymous database that eliminates the security and
privacy concerns of the users. The critical issue of biometric cryptosystem is the
evolvement of an optimal encryption technique that is being capable to handle the
intra-individual variability of the biometric data. That is, we have to devise the
error-tolerant encryption technique for the implementation of biometric cryptosystems for
robust identity verification. In order to bridge the gap between the impreciseness of
biometric data and the exactness of cryptography, different studies have been proposed in
the literature (Soutar et al., 1998; Juels and Sudan, 2002; Hao and Chan, 2002; Clancy
et al., 2003; Hao et al., 2006).
A taxonomy of biometric system vulnerabilities and defences
Figure 8
Schematic diagram of biometric cryptosystem technique used for template protection in
biometric security system (see online version for colours)
Soutar et al. (1998) have among the first who developed an earliest biometric encryption
system that linked and retrieved a digital key using the interaction of fingerprint images.
Juels and Sudan (2002) have proposed a cryptographic construction called a fuzzy vault
that is capable to handle the intraclass variations present in the biometric data. It is
operated in a key binding mode where users place a secret value in a fuzzy vault and lock
it using an unordered set (e.g., minutiae in fingerprints). The ability of fuzzy vault is to
work with the unordered sets and handles the intraclass variations making it a favourable
solution for biometric cryptosystems. Hao and Chan (2002) have proposed a
cryptosystem that generates the secret keys from online signatures. On the database of
25 subjects, they have collected ten signatures for each subject. For each signature they
have defined 43 features like pressure, stroke, direction and speed etc. Feature coding is
used to quantise each feature into bits that are concatenated to generate a strings of 0’s
and 1’s. On an average 40-bits key entropy the system achieved the false non-match rate
of 28%, false match rate of 1.2% and an equal error rate of 8%.
Clancy et al. (2003) have proposed a fuzzy vault scheme for fingerprint and given the
name fingerprint vault. The scheme is based on the location of minutia points which are
recorded as real points form a locking set. A secret key is derived from these minutia
points using polynomial reconstruction. Hao et al. (2006) have proposed a method to
integrate the iris code into cryptographic application. They have shown that the keys are
generated from iris biometric using error-correction that can be changed to produce
different keys. The advantage of producing different keys for different applications is to
make infeasible for an adversary to circumvent all systems simultaneously. The technique
has evaluated on iris images of 70 subjects, with ten images from each eye. On a key
length of 140-bits, an error free key can be reproduced reliably from genuine iris codes
with a 99.5% success rate.
Y.N. Singh and S.K. Singh
3.2.3 Performance evaluation of template protection techniques
The performance of template protection techniques such as template transformation and
biometric-cryptosystem associated to different biometrics are shown in Table 2 and
Table 3, respectively. The performance of template protection techniques is estimated
on different datasets and different test conditions. Analysing the advantages and
disadvantages of each methods it can be observed that the success of the cited template
protection techniques have been limited due to the conditions imposed by many real
applications. This is because the modified template based on the existing schemes
increases the authentication error rate and demands more computation during matching,
which is further compounded by the lack of standards for defining and storing modified
templates. All of the presented techniques have just emerged and it is obvious that time is
required until these techniques are truly applied.
Table 2
Performance estimates of template transformation techniques associated to different
EER (%)
Face (Wang and
Hatzinakos, 2009)
Random projection and
Sorted index numbers
FERET (Phillips et al.,
1998) (1,020 subjects)
(Ratha et al., 2007)
Cartesian, polar and surface
folding transformations
IBM-99 optical
(188 pairs)
Iris (Chin et al., 2006)
S-iris encoding and
pseudo-random number
(108 subjects)
1.00 (2.59)
Speech (Teoh and
Chong, 2010)
Probabilistic random
projection and text
independent verification
(Higgins et al., 1991)
(138 subjects)
LT – 2.98 (5.36)
ST – 4.83 (3.98)
Notes: LT: legitimate-token, ST: stolen-token and N/R: not reported. Equal error rate
(EER) values in brackets show the performance on original samples.
Table 3
Performance estimates of biometric cryptosystem techniques associated to different
Key size
Fingerprint (Soutar et al., 1998)
Fingerprint vault (Clancy et al., 2003)
FNMR: 30%
Ten images of each eye
(70 subjects)
FNMR: 0.5%
Ten signatures each
(25 subjects)
EER: 8%,
FNMR: 28% and
FMR: 1.2%
Iris (Hao et al., 2006)
Online signature (Hao and Chan, 2002)
Notes: EER: equal error rate, FNMR: false non-match rate and N/R: not reported.
Biometric systems are being widely used for reliable identity management, but the
systems themselves are vulnerable to a number of security threats. Biometric security
A taxonomy of biometric system vulnerabilities and defences
systems are recumbent to deliberate or inattentive security lapses that can lead to
illegitimate intrusion, DoS or theft of individual’s sensitive information enrolled in the
system. Among the described vulnerabilities that are related to the development and use
phase of a biometric system, attacks on the stored biometric templates is a major concern.
Because there is a strong linkage between an individual’s template and his/her identity, in
addition the biometric templates are irrevocable. We believe that the available template
protection techniques are not yet matured for handling large scale security applications.
However, the choice of a template protection technique depends on the application
scenario and its requirements.
The vulnerabilities of a biometric system are mainly related to the apparent nature of
the relevant information and limited vitality detection mechanisms incorporated in the
system. It is not hard for an adversary to create a spoof biometric from a biometric
sample of a genuine user or even a stored template is stolen and gain illegitimate access.
Many state-of-the-art vitality detection techniques are known for different biometrics
but it has been suggested that the simultaneously acquisition of multiple biometric
identities from people during enrolment can be a good solution for detecting the vitality
signs from biometric samples. On the other hand, bioelectrical signals such as the
ECG or electroencephalogram (EEG) are emerging as new biometrics for individual
authentication. Study suggests that the impulses of cardiac rhythm and the electrical
activity of brain recorded in the ECG and EEG, respectively show unique features among
individuals, therefore they can be suggested to use as biometric (Singh and Singh, 2012).
The favourable characteristic to use the ECG or EEG as biometric is their inherent feature
of vitality that signify the life signs which is a strong protection against spoof attacks.
To effectively guard against vulnerabilities, different techniques have been proposed
to protect the stored template. Moreover, the design of a template protection technique
depends entirely on the representation of the biometric features. For example, a
non-invertible transform is a good choice for minutia-based fingerprint features while
biometric cryptosystem can be a good choice for a fixed-length binary representation of
iris code. However, if the biometric samples have large intraclass variations then neither
non-invertible transform nor biometric cryptosystem techniques are possible to apply.
Despite the advantages of different template protection techniques, there is no
sustained efforts have been seen for the adoption of such security technologies by the
biometric industry. The reason may be due to lack of standards for designing and storing
modified templates, computationally expensive matching process and increase in
authentication error using modified templates. However, we believe that more secure
techniques will weaken the security threats and provide confidence about the integrity of
the system.
As the use of biometric-based authentication become more popular, the security issue
probably represents the most important concern that has to be addressed during the
design of a biometric authentication system. Biometric systems are vulnerable against a
number of threats. We have classified the threats of a biometric system as faults, failures
and security attacks. A high-level categorisation of the biometric systems vulnerabilities
is presented, in particular a multidimensional environment of vulnerabilities are
represented by Ishikawa’s diagram. To guard against vulnerabilities, the defence
Y.N. Singh and S.K. Singh
techniques such as vitality detection and biometric templates protection are critically
reviewed. In particular, a classification of current state-of-the-art of vitality detection
techniques of commonly used biometrics (e.g., face, fingerprint and iris) is given.
We have critically reviewed the vitality detection techniques and evaluated their
performances on the datasets and the test conditions used for the experiment.
A template protection technique with provable security and acceptable recognition
performance remains to be puzzled. The commonly used template protection techniques
proposed in the literature such as biometric template transformation and biometric
cryptosystem are discussed. The performance of template protection techniques are
estimated on the datasets and the test conditions used in the experiment. We believe that
the available template protection techniques are not yet sufficiently matured for large
scale applications.
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