Biometric Template Security: Challenges And Solutions

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BIOMETRIC TEMPLATE SECURITY: CHALLENGES AND SOLUTIONS
Anil K. Jain Arun Ross Umut Uludag
Michigan State University West Virginia University Michigan State University
East Lansing, MI, 48824, USA Morgantown, WV, 26506, USA East Lansing, MI, 48824, USA
[email protected] [email protected] [email protected]
http://biometrics.cse.msu.edu
ABSTRACT
A biometric system is vulnerable to a variety of attacks
aimed at undermining the integrity of the authentication pro-
cess. These attacks are intended to either circumvent the se-
curity afforded by the system or to deter the normal function-
ing of the system. We describe the various threats that can be
encountered by a biometric system. We specifically focus on
attacks designed to elicit information about the original bio-
metric data of an individual from the stored template. A few
algorithms presented in the literature are discussed in this re-
gard. We also examine techniques that can be used to deter or
detect these attacks. Furthermore, we provide experimental
results pertaining to a hybrid system combining biometrics
with cryptography, that converts traditional fingerprint tem-
plates into novel cryptographic structures.
1. INTRODUCTION
Establishing the identity of an individual is of paramount
importance in several civilian and government applications
where errors in recognition can undermine the integrity of
the system. Example of such applications include interna-
tional border control, access to nuclear facilities, airport se-
curity, issuance of passports or driver licences, etc. Tradi-
tionally, a combination of ID cards (token-based security)
and PINs/passwords (knowledge-based security) has been
used to validate the identity of an individual. These methods
are, however, vulnerable to the wiles of an impostor and can-
not be reliably used in large-scale applications such as border
control, where the throughput is required to be in the order
of thousands of users per day. The advent of biometrics has
introduced a secure and efficient alternative to traditional au-
thentication schemes. Biometrics is the science of establish-
ing or determining an identity based on the physiological or
behavioral traits of an individual. These traits include finger-
prints, facial features, iris, hand geometry, voice, signature,
etc. In conjunction with traditional authentication schemes,
biometrics is a potent tool for establishing identity [1].
A typical biometric system comprises of several mod-
ules. The sensor module acquires the raw biometric data of
an individual in the form of an image, video, audio or some
other signal. The feature extraction module operates on the
biometric signal and extracts a salient set of features to rep-
resent the signal; during user enrolment the extracted feature
set, labeled with the user’s identity, is stored in the biomet-
ric system and is known as a template. The matching module
compares the feature set extracted during authentication with
the enrolled template(s) and generates match scores. The de-
cision module processes these match scores in order to either
determine or verify the identity of an individual. Thus, a bio-
metric system may be viewed as a pattern recognition sys-
tem whose function is to classify a biometric signal into one
of several identities (viz., identification) or into one of two
classes - genuine and impostor users (viz., verification).
While a biometric system can enhance user convenience
and bolster security, it is also susceptible to various types of
threats as discussed below [2, 3].
1. Circumvention: An intruder may gain access to the sys-
tem protected by biometrics and peruse sensitive data
such as medical records pertaining to a legitimately en-
rolled user. Besides violating the privacy of the enrolled
user, the impostor can also modify sensitive data.
2. Repudiation: A legitimate user may access the facilities
offered by an application and then claim that an intruder
had circumvented the system. A bank clerk, for example,
may modify the financial records of a customer and then
deny responsibility by claiming that an intruder could
have possibly stolen her biometric data.
3. Covert acquisition: An intruder may surreptitiously ob-
tain the raw biometric data of a user to access the system.
For example, the latent fingerprints of a user may be lifted
from an object by an intruder and later used to construct
a digital or physical artefact of that user’s finger.
4. Collusion: An individual with wide super-user privileges
(such as an administrator) may deliberately modify sys-
tem parameters to permit incursions by an intruder.
5. Coercion: An impostor may force a legitimate user (e.g.,
at gunpoint) to grant him access to the system.
6. Denial of Service (DoS): An attacker may overwhelm
the system resources to the point where legitimate users
desiring access will be refused service. For example, a
server that processes access requests can be flooded with
a large number of bogus requests, thereby overloading
its computational resources and preventing valid requests
from being processed.
Ratha et al. [4] identified several different levels of at-
tacks that can be launched against a biometric system (Fig-
ure 1): (i) a fake biometric trait such as an artificial finger
may be presented at the sensor, (ii) illegally intercepted data
may be resubmitted to the system, (iii) the feature extractor
may be replaced by a Trojan horse program that produces
pre-determined feature sets, (iv) legitimate feature sets may
be replaced with synthetic feature sets, (v) the matcher may
be replaced by a Trojan horse program that always outputs
high scores thereby defying system security, (vi) the tem-
plates stored in the database may be modified or removed, or
new templates may be introduced in the database, (vii) the
data in the communication channel between various modules
of the system may be altered, and (viii) the final decision
Appeared in the Proceedings of European Signal Processing Conference (EUSIPCO), (Antalya, Turkey), September 2005
Sensor
Feature
Extractor
Matcher
Application Device
(e.g.,cash dispenser)
Stored
Templates
1. Fake
Biometric
2. Replay
Old Data
3. Override
Feature Extractor
Yes/No
8. Override Final Decision
5. Override
Matcher
4. Synthesized
Feature Vector
7. Intercept
the Channel
6. Modify
Template
Figure 1: Vulnerabilities in a biometric system (adapted from [4]).
output by the biometric system may be overridden.
The UK Biometric Working Group (UK-BWG) lists sev-
eral factors that can affect the integrity of the template [5]: (i)
accidental template corruption due to a system malfunction
such as a hardware failure, (ii) deliberate alteration of an en-
rolled template by an attacker, and (iii) substitution of a valid
template with a bogus template for the purpose of deterring
system functionality.
In this paper, we discuss several issues related to template
security. Specifically, we examine some of the attacks that
can be used to compromise template information. Then, we
analyze possible solutions to alleviate this problem.
2. COMPROMISING TEMPLATE INFORMATION
A template represents a set of salient features that summa-
rizes the biometric data (signal) of an individual. Due to
its compact nature, it is commonly assumed that the tem-
plate cannot be used to elicit complete information about
the original biometric signal. Furthermore, since the tem-
plates are typically stored in an encrypted form, it is sub-
stantially difficult to decrypt and determine the contents of
the stored template (without the knowledge of correct de-
crypting keys). Thus, traditionally, template-generatingalgo-
rithms have been viewed as one-way algorithms. However,
in the recent literature there have been techniques presented
that contradict these assumptions.
Adler [6] demonstrated that a face image can be regen-
erated from a face template using a “Hill Climbing Attack”
(attack level 2 in Figure 1). He employed an iterative scheme
to reconstruct a face image using a face verification system
that releases match scores. The algorithm first selects an es-
timate of the target face from a local database comprising
of a few frontal images by observing the match score corre-
sponding to each image. An eigen-face (computed from the
local database) scaled by 6 different constants is added to this
initial estimate resulting in a set of 6 modified face images
which are then presented to the verification system. The im-
age resulting in an improved match score is retained and this
process is repeated in an iterative fashion. Within a few thou-
sand iterations, an image that can successfully masquerade as
the target face image is generated. The important feature of
this algorithm is that it does not require any knowledge of
either the matching technique or the structure of the template
used by the authentication system. Furthermore, template
encryption does not prevent this algorithm from successfully
determining the original face image. The algorithm was able
to “break” three commercial face recognition systems.
Uludag and Jain [3] devised a synthetic template gener-
ator (STG) that also uses the “Hill Climbing Attack” (attack
level 4 in Figure 1) to determine the contents of a target fin-
gerprint template (D
i
) for the i
th
user (see Figure 2). The
minutiae template is assumed to be a sequence of (r, c,
θ
) val-
ues representing the location and orientation of component
fingerprint minutiae. The STG begins by generating a fixed
number of synthetic templates each comprising of randomly
generated minutiae points. These templates are compared
against the target template in the database (via the matcher)
and the synthetic template resulting in the best match score
is retained. The retained template is then modified itera-
tively via the following four operations: (i) the r, c and
θ
values of an existing minutia are perturbed, (ii) an existing
minutia is replaced with a new minutia, (iii) a new minu-
tia is added to the template, and (iv) an existing minutia is
deleted. The modified template (T
i
j
) is compared against the
target template and the match score (S(D
i
, T
i
j
)) computed.
This process, viz., modifying the current synthetic template
and comparing it against the target template, is repeated until
the match score exceeds a pre-determined threshold. The au-
thors used this scheme to break into 160 fingerprint accounts;
their algorithm required only 271 iterations, on an average,
to exceed the matching threshold for each one of those 160
accounts.
Hill [7] describes a masquerade attack wherein the fin-
gerprint structure is determined using the minutiae template
alone (attack level 7 in Figure 1). It is assumed that each
minutia point is characterized using its 2D location, orienta-
tion and the curvature of the ridge associated with it. Based
on minutiae points, the author predicts the shape of the fin-
gerprint (i.e., its class) using a neural network classifier con-
sisting of 23 input neurons, 13 hidden neurons and 4 output
neurons (corresponding to 4 fingerprint classes). However,
Appeared in the Proceedings of European Signal Processing Conference (EUSIPCO), (Antalya, Turkey), September 2005
Template
Database
Fingerprint
Matcher
i
D
Synthetic
Template
Generator
j
i
T
( , )
i i
S D T
Attack
Module
To other
modules
Attacking
System
Target System
i
D
Figure 2: Algorithm to synthesize minutiae templates [3].
the classification performance is rather low (an error rate of
28.9% on a small set of 242 fingerprints). The author then
uses a generic orientation map and the minutiae information
to generate line drawings that are a digital artefact of the orig-
inal fingerprint. The proposed technique is observed to work
on a database of 25 fingerprints from arch class.
Ross et al. [8] propose another technique to elicit the fin-
gerprint structure from the minutiae template (attack level 7
in Figure 1). Each minutia is assumed to be represented by
its 2D spatial location and its local orientation. The authors
identify minutia triplets which are used to estimate the un-
derlying orientation map. The estimated orientation map is
observed to be remarkably consistent with the flow of ridges
in the original (unseen) parent fingerprint. Furthermore, they
use a set of 11 features derived from the minutiae points to
predict the class of the fingerprint. A 5 Nearest-Neighbor
classifier is used to classify the minutiae set of a fingerprint
into one of four classes. Their classification experiment con-
ducted on a dataset of 2200 fingerprints exhibits an error rate
of 18%. Finally, they use Gabor-like filters (suggested by
Cappelli et al. [9]) to generate fingerprints based on the ori-
entation map (Figure 3).
(a) (b) (c)
Figure 3: Reconstructing fingerprints [8]: (a) Minutiae distri-
bution of a fingerprint image, (b) predicted orientation map,
(c) reconstructed fingerprint.
Besides these types of attacks, an intruder may alter the
contents of a template in order to deter a legitimate user from
being successfully verified (attack level 6 in Figure 1).
3. PROTECTING BIOMETRIC TEMPLATES
Several methods have been suggested in the literature to pro-
tect biometric templates from revealing important informa-
tion. In order to prevent the Hill-Climbing Attack from suc-
cessfully converging, Soutar [10] has suggested the use of
coarsely quantized match scores by the matcher. However,
Adler [11] demonstrated that it is still possible to estimate
the unknown enrolled image although the number of itera-
tions required to converge is significantly higher now.
Yeung and Pankanti [12] describe an invisible fragile wa-
termarking technique to detect regions in a fingerprint im-
age that have been tampered by an attacker. In the proposed
scheme, a chaotic mixing procedure is employed to trans-
form a visually perceptible watermark to a random-looking
textured image in order to make it resilient against attacks.
This “mixed” image is then embedded in a fingerprint im-
age. The authors show that the presence of the watermark
does not affect the feature extraction process. The use of a
watermark also imparts copyright capability by identifying
the origin of the raw fingerprint image.
Jain and Uludag [13] suggest the use of steganography
principles to hide biometric data (e.g., fingerprint minutiae)
in host images (e.g., faces). This is particularly useful in dis-
tributed systems where the raw biometric data may have to be
transmitted over a non-secure communication channel. Em-
bedding biometric data in an innocuous host image prevents
an eavesdropper from accessing sensitive template informa-
tion. The authors also discuss a novel application wherein
the facial features of a user (i.e., eigen-coefficients) are em-
bedded in a host fingerprint image (of the user). In this sce-
nario, the watermarked fingerprint image of a person may
be stored in a smart card issued to that person. At an ac-
cess control site, the fingerprint of the person possessing the
card will first be compared with the fingerprint present in the
smart card. The eigen-coefficients hidden in the fingerprint
image can then be used to reconstruct the user’s face thereby
serving as a second source of authentication.
Ferri et al. [14] propose an algorithm to embed dy-
namic signature features into face images present on ID
cards. These features are transformed into a binary stream
after compression (used in order to decrease the amount of
payload data). A computer-generated hologram converts this
stream into the data that is finally embedded in the blue-
channel of a face image. During verification, the signature
features hidden in the face image are recovered and com-
pared against the signature obtained on-line. Ferri et al. [14]
report that any modification of the face image can be de-
tected, thereby disallowing the use of fake ID cards.
Since the biometric trait of a person cannot be easily re-
placed (unlike passwords and PINs), a compromised tem-
plate would mean the loss of a user’s identity. Ratha et al.
[15] propose the use of distortion functions to generate bio-
metric data that can be canceled if necessary. They use a
non-invertible transformation function that distorts the input
biometric signal (e.g., face image) prior to feature extraction
or, alternately, modifies the extracted feature set (e.g., minu-
tiae points) itself. When a stored template is compromised,
then the current transformation function is replaced with a
new function thereby “canceling” the current (compromised)
template and generating a new one. This also permits the
use of the same biometric trait in several different applica-
tions by merely adopting an application-specific transforma-
Appeared in the Proceedings of European Signal Processing Conference (EUSIPCO), (Antalya, Turkey), September 2005
tion function. However, it is not clear how matching can be
accomplished in the transformed domain.
In the realm of template transformation, the so-called
biometric cryptosystems are gaining popularity (for a survey
on existing techniques, see [16]). These systems combine
biometrics and cryptography at a level that allows biometric
matching to effectively take place in the cryptographic do-
main, hence exploiting the associated higher security. For
example, Uludag et al. [17] convert fingerprint templates
(minutiae data) into point lists in 2D space, which implic-
itly hide a given secret (e.g., a 128-bit key). The list does
not reveal the template data, since it is augmented with chaff
points to increase security. The template data is identified
only when matching minutiae data from an input fingerprint
is available. The system is observed to operate at a Gen-
uine Accept Rate (GAR) of 76% with no false accepts on a
database comprising of 229 users.
Although several techniques have been proposed to en-
hance the security of a user’s template, government regula-
tions will also have to be established in order to address the
issue of template privacy. For example, issues related to the
sharing of biometric templates across agencies (e.g., medical
companies and law-enforcement agencies) and the inferring
of personal information about an enrolled user from biomet-
ric data (e.g., “Is this person prone to diabetes?”) have to be
countered by establishing an appropriate legal framework.
4. SUMMARY AND CONCLUSIONS
We have discussed various types of attacks that can be
launched against a biometric system. We have specifically
highlighted techniques that can be used to elicit the con-
tents of a biometric template thereby compromising privi-
leged information. We discuss the importance of adopting
watermarking and steganography principles to enhance the
integrity of biometric templates. Cancelable biometrics may
be used to “reset” the biometric template of a user in the
event that the user’s template is compromised. Also, bio-
metric cryptosystems can contribute to template security by
supporting biometric matching in secure cryptographic do-
mains.
Smart cards are gaining popularity as the medium for
storing biometric templates. As the amount of available
memory increases (e.g., state-of-the-art smart cards have 64-
KByte EEPROM), there is a propensity to store more infor-
mation in the template. This increases the risks associated
with template misuse. As a result, the issue of template secu-
rity and integrity continues to pose several challenges, and it
is necessary that further research be conducted in this direc-
tion.
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Appeared in the Proceedings of European Signal Processing Conference (EUSIPCO), (Antalya, Turkey), September 2005