This document is a student's dissertation submitted as part of their master's degree program. It examines the use of fingerprint verification on contactless smart cards for physical access control systems. Specifically, it evaluates the security advantages and limitations of a "match on card" system by considering potential attacks an insider attacker could perform. It discusses low-cost attacks such as spoofing the fingerprint sensor, replay attacks across the contactless interface, and template extraction attacks. It also covers countermeasures like template protection through cryptographic techniques and feature transformation with salting and non-invertible functions. The goal is to assess the overall security of match on card verification given resource constraints on smart cards and a generic system architecture.

1. Student number: 100543130
Submitted as part of the requirements for the award of the MSc in Information
Security at Royal Holloway, University of London.
I declare that this assignment is all my own work and that I have acknowledged
all quotations from the published or unpublished works of other people. I declare
that I have also read the statements on plagiarism in Section 1 of the Regulations
Governing Examination and Assessment Offences and in accordance with it I
submit this project report as my own work.
1

2. Fingerprint Match on-Card Verification In
proximity card based Physical Access Control
Systems - Low Cost Attacks and
Countermeasures
Michael Conway

3. Executive Summary
Match on-card is an area of developing interest within the domain of authenti-
cation. Traditional methods of authentication within Physical Access Control
Systems (PACS) use simple access tokens or in areas where access must be more
heavily restricted, biometric verification. Match on-card (MoC) with the use of
fingerprints is developing as a popular method of verifying a user whilst bind-
ing them to their identity and using the tamper resistent features of a smart
card. Because of its advantages of speed and durability, a proximity interface
circuit card (PICC) is commonly used within access control systems. In fact, the
combination of proximity tokens with a MoC system offers a reasonably good
compromise between the security of a template and the affects of constrained
resources within such cards. This project assesses these advantages by looking
at a generic system with its most common aspects derived from what is afford-
able at the present time. Using the profile of a potential insider with access to
only moderate resources, some low-cost attacks pertinent to a MoC system are
discussed, highlighting where insider knowledge can prove an advantage. While
match on-card may be seen to bind a user to an identity, the attacks presented
demonstrate that templates may in fact be potentially vulnerable to compro-
mise. The issue of template protection is by no means simplistic or absolute.
Therefore the provision of security whilst balancing cost and convenience po-
tentially involves the same kinds of considerations as per traditional models of
2 factor authentication that use knowledge based identity tokens.

4. Contents
1 Introduction 4
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Structure of the dissertation . . . . . . . . . . . . . . . . . . . . . 7
1.3 Statement of Objectives . . . . . . . . . . . . . . . . . . . . . . . 7
2 Key Concepts 8
2.1 Physical Access Control Systems . . . . . . . . . . . . . . . . . . 8
2.2 What is a Smart Card? . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Contactless Cards . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4 Principle Contactless Card Standards . . . . . . . . . . . . . . . 12
2.5 ISO 14443 - Proximity Coupling . . . . . . . . . . . . . . . . . . 13
2.6 Biometric Authentication . . . . . . . . . . . . . . . . . . . . . . 14
2.7 Errors in Biometric Authentication . . . . . . . . . . . . . . . . . 15
2.8 Fingerprint-based Authentication . . . . . . . . . . . . . . . . . . 16
2.9 On-card Verification Strategies . . . . . . . . . . . . . . . . . . . 17
2.9.1 Template on-Card . . . . . . . . . . . . . . . . . . . . . . 18
2.9.2 Match on-card . . . . . . . . . . . . . . . . . . . . . . . . 18
2.9.3 System on-card . . . . . . . . . . . . . . . . . . . . . . . . 19
3 The Context 20
3.1 Resource Limitations on a Smart Card . . . . . . . . . . . . . . . 20
3.2 Resources on the Microcontroller . . . . . . . . . . . . . . . . . . 21
3.2.1 Processing Capability and Clock signal . . . . . . . . . . . 22
3.2.2 Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 Data Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.4 Impact of Constrained Resources . . . . . . . . . . . . . . . . . . 24
4 Attacks and Countermeasures 25
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.1.1 The MoC PACS system . . . . . . . . . . . . . . . . . . . 26
4.1.2 The Attacker Profile . . . . . . . . . . . . . . . . . . . . . 27
4.1.3 The Generic System . . . . . . . . . . . . . . . . . . . . . 27
4.2 Applicable attack routes . . . . . . . . . . . . . . . . . . . . . . . 28
4.3 Spoofing attacks on the sensor . . . . . . . . . . . . . . . . . . . 29
4.3.1 Anti-spoofing countermeasures and exploitability . . . . . 30
4.3.2 Feasibility of Spoofing Attacks . . . . . . . . . . . . . . . 31
4.4 Attacks across the Contactless Interface . . . . . . . . . . . . . . 32
4.4.1 Replay Attack . . . . . . . . . . . . . . . . . . . . . . . . 32
1

5. 4.4.2 Relay Attacks and Countermeasures . . . . . . . . . . . . 33
4.5 Brute Force Attacks . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.6 Hill Climbing Attacks . . . . . . . . . . . . . . . . . . . . . . . . 35
4.6.1 Injection Attacks . . . . . . . . . . . . . . . . . . . . . . . 37
4.7 Template Protection . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.8 Feature Transformation . . . . . . . . . . . . . . . . . . . . . . . 39
4.8.1 Salting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.8.2 Non-invertible function . . . . . . . . . . . . . . . . . . . 41
4.9 Biometric Cryptosystems . . . . . . . . . . . . . . . . . . . . . . 42
4.9.1 Key Generation . . . . . . . . . . . . . . . . . . . . . . . . 43
4.9.2 Key Binding . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.9.3 Outline of Template Security . . . . . . . . . . . . . . . . 45
5 Summary and Conclusion 47
5.1 Summary of the Overall Security . . . . . . . . . . . . . . . . . . 47
5.2 Other Developments with Match on-Card . . . . . . . . . . . . . 47
5.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
5.4 Summary of Objectives . . . . . . . . . . . . . . . . . . . . . . . 49
6 Appendix 51
6.1 Carrier Channel Modification . . . . . . . . . . . . . . . . . . . . 51
6.2 Anticollision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
6.3 Data transmission . . . . . . . . . . . . . . . . . . . . . . . . . . 53
6.4 Challenge-response Protocol . . . . . . . . . . . . . . . . . . . . . 54
6.5 Dependencies for the Application Specific Transformation Func-
tion Proposed by Cambier . . . . . . . . . . . . . . . . . . . . . . 55
Bibliography 55
2

6. List of Figures
2.1 A typical access control system [12] . . . . . . . . . . . . . . . . . 10
2.2 Diagram of an ID-1 smart card [62] . . . . . . . . . . . . . . . . . 12
2.3 Processing steps for enrollment, verification and identification [40] 15
2.4 FMR vs FNMR (extracted from [76]) . . . . . . . . . . . . . . . . 16
2.5 Three Strategies for Fingerprint Verification (extracted from [44]) 18
4.1 A MoC PACS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2 Hill Climbing Attack System [150] . . . . . . . . . . . . . . . . . 36
4.3 Template Protection . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.4 Authentication Process Using Feature Transformation [80] . . . . 40
6.1 Challenge-Response Protocol [28] . . . . . . . . . . . . . . . . . . 54
6.2 Application Specific Transformation Function [31] . . . . . . . . . 55
3

7. Chapter 1
Introduction
This section specifically describes the motivation behind this topic and how the
subject area can add further value to the wider field of access control. This will
include a brief introduction to the topic as a way of leading on to the main body
of the project.
1.1 Motivation
This project discusses the use of fingerprint verification on contactless (proxim-
ity) cards with microprocessors for use within Physical Access Control Systems
(PACS). It will evaluate the potential advantages and limitations in terms of
security within such a system. This will be done specifically by way of consid-
ering a range of potential attacks that may be performed, when presented with
a specific attacker profile and a generic match on-card architecture - typical of
that within constrained embedded systems, as seen within the current market.
As physical access control concerns the management of direct access to an
area or building, it should be appreciated that it is an essential element in the
overall protection of critical assets. Such systems are generally seen alongside
myriad perimeter (access) controls in various physical locations including private
organisations, public attractions or transportation facilities and high security
government buildings, where control of access requires regulation. Although
they are generally not considered catch-all solutions, they are often deployed
alongside other first line perimeter security controls including wire fences, secu-
rity guards, time controlled door locks and surveillance equipment. In practice,
electronic PACS are used to regulate access on the basis of predefined access
profiles or access control lists (ACLs). These ACLs can be used to support
any particular security policy by correctly authorising access to one or more
location(s), following on from an initial positive identification.
Contactless tokens are commonly used within PACS because of the enhanced
speed, robustness and convenience as a consequence of not having to closely posi-
tion or orientate the smart card to communicate with its reader. This technology
also seems to resolve some of the problems of contamination or degradation of
contact parts, as is pertinent to contact-based smart card, which may be dam-
aged by electrostatic discharge [22]. These cards are used at longer distances
(close-coupling RFID systems are the exception[48]), the magnitude of which
4

8. varies according to the type of system used. This permits users to quickly es-
tablish access through an entry point(see [69]) which is further advantageous as
the amount of time required for communication between an external terminal
and smart card is reduced, as attributed to the enhanced transmission speeds
supported by contactless card standards.
However, one issue with the majority of PACS is that they tend to only use
single, token-based authentication for access. This may not be an issue within
low-security environments, but where access needs to be restricted carefully on
the basis of identity, this is certainly an issue. It has been long recognised
that a token can be “lost, stolen, forgotten or misplaced”[27], which can be-
come a significant security risk in such an environment. The alternative,“two-
factor” authentication, involves combining an identity token (something you
have) representing a claimed identity and a second factor. This second factor
has traditionally been a memorable credential such as a password, passphrase
or PIN (something you know). Regardless, passwords are frequently forgotten,
and therefore as a counter-step to resolve this, they are often made simple and
predictable; their overall management can, therefore, be expensive [75]. Those
passwords that are stored electronically are prone to brute force, else they can be
dictionary attacked with relative ease, depending on length, without requiring
the use of particularly advanced hardware or computationally intensive pro-
cessing [88]. Moreover, these specific factors only partly answer the question “is
someone who they claim to be?” a question which encompasses the essence iden-
tification. Possession or knowledge is an unreliable and circumstantial indicator
of identity.
The use of tokens or “object-based authenticators” combined with an “ID-
based authenticator”[119], may add an additional level of security by identifying
someone on the basis of their unique biological traits[67]. The perceived advan-
tage of this approach is that it is difficult for biometric credentials to be lost,
forged, forgotten, shared or easily acquired; unless the biometric authentication
subsystems are manipulated, only an enrolled person can be verified[78]. The
advances in general communication technologies, and the frequency in which
people travel between physical locations has prompted the use of biometrics
as a way of automatically and conveniently establishing identity. Biometric au-
thentication is one potential way of circumventing any requirement to hold large
databases of stored PIN numbers or passwords (hashes), where localised storage
of an enrolled template can be adopted instead. Furthermore, biometric authen-
tication has been considered to be a reliable, trusted means of binding the owner
of an identity record to that record [84]. The degree to which this is achieved
within a PACS depends on the additional credentials or protection mechanisms
stored on a card[43], and whether the biometric authenticator originated from
that person whom was present at the time of verification.
Owing to the maturity [27] of its usage and the relative uniqueness and per-
manence of fingerprints, this method of authentication is considered one of the
more reliable forms of biometric authentication, and is well understood. Finger-
prints satisfy the “7 Pillars of biometric wisdom” for the particular reasons that
they are mature, well understood, reasonably resistant to change and unique[27].
Fingerprint sensors are relatively cheap compared to others and therefore they
have been used in high security environments; in the United States Department
of Defense(DoD) they are the dominant biometric authenticator[118]. Although
some have questioned the efficacy and scientific foundation of fingerprints as a
5

9. method of uniquely identifying people[121][34] it is indisputable that as a bio-
metric method or modality it has been widely studied, and furthermore, it has
been used as a contemporary means of identification within automatic identi-
fication applications [122]. Certainly high levels of accuracy for this mode of
biometric authentication have been shown to be demonstrated [96] and it is the
first type of biometric introduced into true match on-card technology [21]. This
provides a strong justification as to why this biometric has been chosen, above
others, to be included within the framework of discussion.
In terms of the various configurations used for verification using a card,
match on-card seems the most ideal in terms of balancing the protection of a
biometric template on a card, and accounting for resource limitations in line with
those at present time. In this configuration specifically, the tamper-resistant
environment of the card is used to both house the template and perform the
match function. This reduces the attack surface by ensuring that the tempate
remains on the card, and only the result of a matching- or live- template is sent.
Storing the card also removes any overhead from maintaining large databases
of templates within potentially insure databases.
However, there are several constraints and limitations in terms of the re-
sources available to a card. Among these limitations are the amount of power
and clock provided by the card, as well as further limitations within the RAM,
ROM, EEPROM and transmission speed, as distinct from conventional PC ar-
chitectures. The architecture and implementation of such a solution can there-
fore vary, and similarly the potentially insecure contactless interface may be
exploited. All of these aspects have a direct bearing on the time and reliability
of the verification - and therefore - end-to-end process of access control us-
ing fingerprint verification within a smart card. Furthermore, it is the case that
both the contactless communication interface and biometric system components
possess vulnerabilities that may be exploited.
It should be noted that at the time of writing there are a few major devel-
opments progressing that have combined these aspects.
1. Within the US, there are some developments occuring:
• The U.S Department of Defense (DoD) has taken an interest in this
area since the passing of the U.S Homeland Security Presidential
Directive 12 or HSPD-12 [153]. Personal Identification Verification
(PIV) cards required to be compliant with the follow-on U.S. stan-
dard FIPS-120 are one particular example of where development in
this area is taking place.
• The contactless version of the Common Access Card, CAC [117] is-
sued by the DoD to the DoD community, is another example of a
development combining the use of on-card verification with contact-
less technology. Early tests focused mainly on template on-card ver-
ification, but since then it was demonstrated that these cards can
perform match on-card across an encrypted channel [136] and sup-
port for match on-card over a contactless interface exists within the
next-generation cards.
2. One of the most pertinent examples of where this combination of tech-
nological factors is being researched is within the Europe Union, under
6

10. the multi-million pound funded “Turbine” Project [51]. The objective of
this project is to research and ultimately develop e-identity solutions that
incorporate fingerprint-based biometric authentication.
With these developments in mind, the principle aim of this project is to
highlight some of the restrictions involved in incorporating fingerprint verifica-
tion onto a PICC and how the compromises made to the architecture, in order
to balance cost with practicality, can result in the presence of vulnerabilities
within PACS. The specific areas of focus, to this end, will be the attacks that
exploit these vulnerabilities, and those which are low cost and unique to match
on-card, as well as the various means in which these may be mitigated.
1.2 Structure of the dissertation
This project will closely follow the objectives as set out below, beginning in
Chapter 2 with the key concepts behind a contactless MoC implementation
within a PACS. This lays out the the concepts and technologies behind the
various components of generic contactless access control and biometric systems.
Chapter 3 discusses the resource limitations on embedded smart card systems
and how match on-card offers a balanced approach that considers these, given
the challenges of integrating fingerprint verification onto a card in line with
resource constraints. It also details the compromises made on such a system.
Chapter 4 discusses a range of attacks that can be conducted against a
generic solution, in accordance with a specific attacker profile, as well as the
countermeasures that can be implemented against such attacks. This will in-
clude an analysis of how realistic these possibilities are in relation to both the
robustness of the system and the resources of the attacker. Various assumptions
and exclusions will be applied to scope this environment.
The final chapter, Chapter 5, will conclude with a summary evaluation of the
relative levels of security as a consequence of match on-card implementations,
their practicality for access control environments and some of the activities being
done to harmonise/standardise efforts to develop this area across industry.
1.3 Statement of Objectives
1. To discuss the main resource constraints on microprocessing proximity
cards and how this can lead to vulnerabilities.
2. To discuss a range of low-cost attacks that are applicable to the environ-
ment being considered.
3. To discuss the various countermeasures to the above attacks and potential
ways to secure templates.
4. To evaluate the potential security advantages or disadvantages of MoC
implemnentations.
7

11. Chapter 2
Key Concepts
This chapter presents a background to the underlying concepts and technologies
behind a physical access control system using fingerprint-based verification on
a contactless smart card. The concepts within this section will be:
1. Access Control Systems.
2. Smart cards - what they are, their properties, and the types of smart card.
3. Proximity Coupling - the technology most frequently used by contactless
access tokens.
4. Biometric Authentication - including the subtypes identification and ver-
ification.
5. Errors in Biometric Authentication.
6. Fingerprint-based Authentication.
7. Verification Strategies.
2.1 Physical Access Control Systems
In Chapter 1, it was briefly explained that access control is the automated
process of authorising and granting access to a physical location. To build
on this definition, a reference should be made to the international standard
ISO/IEC 10181 part 3[72], which specifies a security framework for access control
in open systems. This defines access control as “The process of determining
which uses of resources within an Open System environment are permitted and,
where appropriate, preventing unauthorized access”. As the scope of this project
concerns physical access control, this definition should be applied to the more
focused remit of physical access control environments.
In general, this process starts when a user presents an authenticator to a
reading device attached adjacent to (or more usually - directly) at the access
point. This reading device is responsible for capturing information from the au-
thenticator; typically a chip card, and passing this on to a portal∗
. During this
∗The term portal is an alternative to control panel, as per the definition within[32]
8

12. process, the smart card may or may not trust the reading device and therefore
further validation or authentication mechanisms may be required. The portal
then communicates further with the additional access control subsystems to
authorise access, the set up for which may vary. In many access control sys-
tems, portals are connected to host computers which are themselves connected
to back-end databases or access control servers (usually containing encrypted
and/or hash-protected data), against which a live credential is compared. Alter-
natively, the reader itself may have enough logic to authenticate the presented
credentials. This matching function is normally carried out at the application
layer using matching software resident either on the host computer, or within an
embedded/smart card operating system, depending on whether more enhanced
authentication mechanisms are used.
Once there has been a positive identification match, the embedded device
or terminal communicates with the portal and transmissions are sent to a door-
release mechanism or “door strike” and an aural sound may be produced, in-
dicating that access has been granted (or in some cases denied). Figure 2.1
illustrates this process.
In contactless physical access control systems incorporating microprocessing
cards, the door reader and card communicate via radio frequency (RF) technol-
ogy, under which the most widely used technology - proximity coupling, is used,
as described in 2.5. In this case, the contactless card is presented within the
RF field and is powered by, and communicates with the reader. Any additional
factors - PIN or biometric - may be used in combination, and tend to transmit
by a separate interface, commonly a serial interface.
Regardless of whether or not authentication is performed within the closed
environment of an embedded system, access to each physical location can be de-
fined and enforced by the use of specific access control lists (ACLs), a type of ac-
cess control scheme used enforce a system specific policy (SSP). Such a policy is
often derived from an overarching enterprise information security policy (EISP)
or other high-level security policy, depending on the type of organisation[26].
Higher security environments often employ the use of formal access control
frameworks supported by granular ACLs, which should be under strict con-
trol. This is in order that access privileges are updated, restricted or revoked
when necessary. As within the framework specified under [72], access control
mechanisms utilise access control information (ACI), including ACLs, which is
used to make a decision by an access control enforcement function (AEF), which
is itself mediated by an access control decision function (ADI). Although these
components are logically separate, they may be part of the same component
within an access control system. For example (as in this case), the system
may be configured so that the AEF and ADF are on the control panel and the
ACI/ACL information exists on the smart card or backend server.
2.2 What is a Smart Card?
Smart cards are currently widely used across several industries, and have been
adopted to suit many purposes, in particular tokens used for authentication and
access control. A generalised definition of a smart card is “a plastic card the size
of an ordinary credit card with a chip that holds a microprocessor and a data-
storage unit”[68], which is a useful but simplistic description of a smart card.
9

13. Figure 2.1: A typical access control system [12]
Smart cards have been around for some time the first example of which was
the “Diners Club” card, a payment card used in the travel and entertainment
industry[124]
Smart cards can be broadly grouped into 2 major categories, the first of
which is known as the “dumb” smart card because of its limited processing
capabilities. This category includes the “memory card”, the first example of
a card containing a microprocessor chip, which contains only memory modules
and practically no CPU power. A key component within the memory card
architecture is the security logic, which mediates memory(ROM and EEPROM)
access. As its name suggests, it is responsible for providing additional security
within the chip and its activity ranges from simple write protection to basic
encryption functions in more advance adaptations.[124]
The second group of smart cards are the “true” smart cards, which have the
extended ability to perform processing functions, carried out by an embedded
smart chip processor (CPU).[49]. State-of-the-art microprocessing cards have a
far greater processing capacity and more memory than dumb cards, and these
levels are advancing. The higher quantities of memory and processing capability
can support many advanced additional features such as multiple applications
and high-level programming languages (APIs) to support specialised applica-
tions including match on-card implementations. This is distinct from the dumb
smart cards. Advanced microprocessor smart cards also support fairly advanced
coprocessors “utilized for accelerating the computation of time-critical proce-
dures relieving the systems microprocessor from these application parts”[53].
Many examples of these exist[110] including cryptographic coprocessors, which
have been developed to support both symmetric and public key cryptography
[48]. In the latter case, this is done by carrying out performance-intensive mod-
ular exponentiations for cryptographic algorithms such as RSA.[65] These cards
consist of operating systems that are multi-threaded and capable of multitask-
ing, ensuring that data stored on the cards need not leave the card itself.[22]
10

14. Match on-card implementations can only be supported by these advanced
cards because of the resources required on what are, in general, constrained
and limited processing environments. Such limits, and their effects on on-card
verification, will be discussed in 3.1.
However, generally speaking, a smart card should satisfy the following qual-
ities [103]:
1. It can participate in an automated electronic transaction.
2. It is used primarily to add security.
3. It is not easily forged or copied.
4. It can store data securely.
5. It can host/run a range of security algorithms and functions.
2.3 Contactless Cards
Within the category of true smart cards are the “Contact” and “Contactless”
cards, both of which require a smart card terminal or reader for the purpose
of rendering data to and from the host, and powering the smart card chip. In
general, cards with IC chips will have 8 electrical contacts (C1 to C8) each of
which with a specific purpose as according to the standard ISO/IEC 7816 part
2. These cards are so-called as they must be inserted into an external smart
card reader, whereupon there is physical contact between the electrical contacts
within the reader and those on the smart card module, with which they are
interfaced (aligned). In order for the card to be correctly read, this process also
requires that efforts are made to correctly orientate it.
However, contactless cards contain an additional communications interface,
most commonly a Radio Frequency (RF) interface. This employs the use of
electronic devices attached to objects or hosts, using either RF or magnetic field
variations to communicate data [56][48]. The main components that perform
the communication dialogue are an external reading device containing a control
unit and an RF module, and a transponder device responsible for carrying data,
containing a microchip. Both of these components have coupling devices that
communicate across an RF channel, so that data can be transferred both ways.
Rather significantly, there is a difference between RF identification in the
context of RFID tags and the use of RF technology in PACS (smart) tokens [13].
These latter smart devices typically should be consistent with the definition
“intelligent read-write devices capable of storing different kinds of data and
operate at different ranges” as in [12]. Simple contactless tokens tend to be little
more than state machines, but as stated, cards that are used for match on-card
verification have operating systems and advanced processing capabilities.
An additional assumption that has been consistently held for some time is
that smart cards predominantly fall into the ID-1 card format family , which
consists of dimensions 85.6mm x 53.98mm x 0.76mm (see figure 2.2)[62]. This
is as defined by the international standard ISO 7810-1 [71]. However, there are
other smaller card formats that also exist, including the ID-000 format, which at
the lower end of the scale can be 25mm x 15mm, and the ID-00 card, dimensions
for which are an intermediate of the formative[87]. However, the most widely
11

15. Figure 2.2: Diagram of an ID-1 smart card [62]
used access control and identity tokens to date still fall within this ID-1 format.
The physical format and properties of the card are of significance, because they
impact the overall availaility of resources within a card (see 3.1).
2.4 Principle Contactless Card Standards
There are 3 main international standards for contactless systems, all operating at
a high frequency band: (1) ISO/IEC 14443 - Proximity Coupling, (2) ISO/IEC
15693 - Vicinity Coupling and (3) ISO/IEC 18092 - Near Field Communication,
all of which use the operating frequency of 13.56MHz. †
. The signal interfaces,
protocols and operating ranges of these system are specified in each of the
standards, which also have the following features:
• Read/write capability including the capacity to store biometric templates
and PINs
• Capability to add security features (although not explicitly designated)
including cryptographic algorithms and digital signatures
• Support for multiple technologies and interfaces
• Authentication between the contactless reader and the contactless card
†Close-contact cards have not been specified as they require precise orientation and are
not in the HF range
12

16. 2.5 ISO 14443 - Proximity Coupling
Of all of these standards, ISO/IEC 14443[1] is the most widely used for con-
tactless systems [142] and has been described as “the standard of choice for
e-passports, credit cards and most access control systems.”[103]. In
practice, however, compliance against this standard is most often obtained for
card readers, due to the sheer variety and volume of contactless cards that are
produced.
Part 1 of the standard defines the physical properties of the card including
physical tolerances and environmental stresses, and its dimensions as in line
with the ID-1 format are specified in ISO/IEC 7810 part 1[71].
The second part [2] specifies RF power and signal interface, including details
regarding data modulation and bit representation (coding). Specifications for
the initialisation of communication, use of commands and the data byte format
of frames are included in part 3, as well as anticollision methods. Finally, Part
4 [4] defines the transmission protocols.
These systems operate at a range of 0 to 10cm and consist of notional
Proximity Integrated Circuit Cards (PICCs) and Proximity Coupling Devices
(PCDs) which transfer data using “proximity coupling” [48]. Under the
standard, PICCs are passive tokens, deriving their power and clock from PCDs,
and transferring data across an alternating high-frequency electromagnetic field
before communicating across the channel. Power is provided by looped coils of
wire in the PCD antenna (consisting of 3-6 windings) when current is applied to
the circuit and the electromagnetic field is produced. When the PICC is in the
field range of the PCD, power is transfered across to the PICC transponder coil
as a result of magnetic flux transfer. Resonant transponder coils and the capac-
itor within the PICC then form a circuit, which is powered at a transmission
frequency equivalent to that of the PCD. This sets up a carrier channel between
the PCD and PICC, along which the PCD can transfer data using direct data
modulation, which alters the baseband signal of the carrier. In the reverse di-
rection, load modulation is used to alter impedance in the antenna of the PCD,
i.e. the PCD is induced to carry out amplitude modulation by responding to
the feedback generated in antenna. ∗
The exact approach of data modulation differs according to 2 disparate sig-
naling interfaces specified in part 2 of the standard: type A and type B. Type
A is used for the majority of contactless smart cards and differs from type B in
3 main areas [2]:
• The exact method for modulating the magnetic field
• The bit/byte coding
• The anticollision method
The latter of these - anticollision - is another important aspect covered by
the standard, in part 3. A collision is the term associated with interference
between data blocks of a PICC when more than one PICC is present within the
interrogation field of a PCD. Clearly this can be an issue as it is not desirable
for data to be corrupted, which can significantly impact verification times and
authentication of PICCs to the PCD. Anticollision measures are important in
∗see 6.1 for further details
13

17. ensuring that individual PICCs can be chosen for communication as necessary,
which is of significant importance - multi-access for PICCs is essential in an
access control environment where many PICCs may be present. See 6.2 for
more details.
2.6 Biometric Authentication
Biometric authentication is the process of identifying a person according to
individual behavioural or physical(physiological) characteristics [105], which is
carried out by a “biometric system.” A biometric system carries out the identifi-
cation process by acquiring raw data from a person using a sensor(data acquisi-
tion), converting it into digitised data and then further processing it to generate
a template representative of a distinctive feature set, in a process known as fea-
ture extraction [79]. While the template is being extracted at any one time, the
template is referred to as a live template.
During enrollment the template may undergo processing to ensure that the
quality is of an acceptable saliency∗
before it is then stored, either in a stor-
age subsystem such as a database, or in a smart token(as is the case for the
MoC solution being considered). This template is often described as a reference
template. The authentication process then takes place and one or more refer-
ence templates are compared with a single live template representing a claimed
identity, using a biometric feature matcher [76].
The outcome of the matching stage is a quantifiable measurement of the
degree of similarity between templates - known as a matching score, or a binary
decision (positive or negative access). In general, there are five major subsystems
(modules) in a generic biometric authentication system responsible for carrying
out these steps: (1) sensor, (2) feature extractor, (3) storage subsystem, (4)
matching module (5) decision module [80].
The two subtypes of authentication method are identification which involves
a 1:n comparison and verification, a 1:1 comparison[57]. In other words, the
former compares a live identity with several stored reference templates (used
commonly in law enforcement when attempting to identify an individual from
a pool of credentials) and the latter with a single reference template. Of these
types, it is a verififcation system that will be the focus in this project.
Figure 2.3 is a simplified diagram of a generic biometric authentication sys-
tem, showing the same basic steps that apply to both verification and identifica-
tion. This illustrates that during verification an identity is claimed (such as by
a PIN) and a single live template representing the claimed identity is compared
with a stored template corresponding to the genuine identity. In the example
given in figure 2.2, the reference template is stored on a database, although
where biometric verification using a smart card is employed, this reference tem-
plate is typically stored on a card.∗
.
∗preprocessing also applies to generation of a live template
∗In 2.9 various types of on-card verification strategy will be discussed
14

18. Figure 2.3: Processing steps for enrollment, verification and identification [40]
2.7 Errors in Biometric Authentication
Biometric authentication based on physiological features is wide-ranging and
includes fingerprint, iris, retinal, facial, vein-pattern and hand geometry recog-
nition among the most popular methods. Regardless of the feature, or biomet-
ric mode, there are considerations regarding their performance that have to be
taken into account, especially when designing a system. In any biometric sys-
tem, it is unlikely that genuine live samples will be consistently identical, as
they are affected by a range of issues [75] particularly those resulting from the
inconsistent presentation of the trait and background noise/distortion. Each
system is therefore designed with a particular tolerance threshold, below which
a sample is rejected. By its own very nature, the biometric matching process
is probabilistic [23] and results in errors occurring, which are affected by the
threshold levels that are set. This gives rise to two major types of error: false
match or false non-match errors. The probabilities of either occurrences are
respectively expressed as the “false match rate”(FMR) and “false non-match
rate” (FNMR)[79].∗
Both rates are influenced by the threshold of the system so
that as it decreases, more false matches are tolerated, ergo the false match rate
increases; and the same is true of the opposite. Various attempts have thus been
made to formulate consistent methods to calculate error rates [152] and assess
overall system performance [96]. One way in which this type of assessment is
illustrated is by the use of a Receiver Operating Characteristic (ROC) curve,
which plots the FMR against FNMR at different operating points [77]. The
error rate at a particular threshold when both of these rates are equal, is known
as the Equal Error Rate (EER) and is a useful indication of the accuracy of a
biometric system as illustrated in figure 2.4.
The trade off between FMR and FNMR is an important consideration for the
Match on-Card solution, since any successful attempts made by an impostor, to
gain access, will warrant an increase in the tolerance threshold of the system.
This could potentially impact the convenience of the access control system if an
∗there other kinds of error - failure to enroll (FTE) and failure to acquire (FTA). [26],[99]
15

19. Figure 2.4: FMR vs FNMR (extracted from [76])
unacceptable number of false non-matches results from such a change.
2.8 Fingerprint-based Authentication
Fingerprint-based authentication appertains to the recognition of fingerprints,
the unique features displayed on the epidermis of a digit(or finger), which are
formed during early fetal development [18]. The features of the digit include
papillary ridges and furrow (valley) patterns, from which singularities and more
express features of the ridges are derived(minutiae) among which are ridge end-
ings, bifurcations and lakes [74].
The process of fingerprint authentication involves the same generic steps and
subsystems as in 2.6. A basic overview of this process shall be described in this
section.
The processes of both enrollment and authentication involve similar basic
steps. During the process of fingerprint image data acquisition, a human digit
is presented to a fingerprint sensor responsible for reading the biometric feature
and converting it to raw data (image) using a fingerprint sensor. There are
numerous sensing technologies (as will be discussed in 4.3), the majority of which
fall within the optical and solid-state families, each using distinct techniques to
capture fingerprint images.
Prior to feature extraction, an optional quality assessment module may be
used to assess the image for variations in quality such as broken or unseparated
ridges, image contrast, ridge aberrations and other varying conditions [80][79].
16

20. In many cases this module assigns a quality metric between 0 and 1 [27], in-
creasing in terms of accuracy. Quality assessment subsystems are common in
traditional AFAS systems as opposed to embedded systems, again due to re-
source constraints.
Once a fingerprint has been scanned by a fingerprint sensor, it typically
first digitised and then binarised, where a low pass filter is used to smooth
high frequency image regions thereby improving clarity and circumventing noisy
areas of the fingerprint and background [154]. This can be performed by one of
many mechanisms, for example those based on normalisation (to a “pre-specified
mean and variance”, local orientation and frequency variations or contextual
filtering [79]). The image can then be further enhanced by refinement into a
thin skeleton, of width one pixel, from which features can be extracted [109][44].
The template signal is next passed to a feature extraction subsystem to
extrapolate features and render the signal into a format suitable for match-
ing. Generally representations are based on spacial locations corresponding
to orientation and the type of minutia[77][109] used in point-matching. How-
ever, there are alternative approaches such as those involving algorithms, which
act on the number of ridges per distance (ridge density), pattern class, rota-
tion and shift[128]. Novel approaches have been proposed which involve using
characteristic features verging the minutiae, such as notional adjacent feature
vectors[146], which have unique global transformations such as rotation and
translation. The majority of these approaches tend to be based on proprietary
algorithms.
During enrollment, the selected minutiae points (usually between one and
twenty) are stored within an enrolled template [108], which can then be used
for comparison in the matching module, during the authentication stage. This
is carried out by a decision making subsystem, and is often based on the degree
of accurate matching of minutiae points. One such way this can be done is by
dividing the number of successfully correlated points by the total number in a
template. This gives a metric between 0 and 1 (as previously described) where
1 is a 100% match and a threshold is chosen within this range, under which
access authentication is denied.
2.9 On-card Verification Strategies
Three dominant strategies exist for verification using a smart card[44], all differ-
ing in how the template is used and the location in which the matching process
takes place as in figure 2.5.
• (i) Template on Card (ToC), otherwise known as Storage on-card.
• (ii) Match on-Card(MoC)
• (iii) System On-Card (SoC), as illustrated in figure 2.5.
.
All three of these strategies differ in how and where the verification process
takes place.
17

21. Figure 2.5: Three Strategies for Fingerprint Verification (extracted from [44])
2.9.1 Template on-Card
Template on-Card (ToC) - also known as storage on-Card, is a form of biometric
verification whereby the smart card acts simply as a storage device that holds
the template. In this configuration, the matching is not done within the smart
card; the template is instead transmitted to an external device that performs
all functions required for matching i.e. image scanning, feature extraction and
matching etc.
Where a positive match has been made by the terminal within a PACS, the
terminal will securely pass the result to the other subsytems. Hence beyond
the transmission of the template, the (PICC) card is no longer involved for that
instance of verification.
Consequently, this is the least secure of the three strategies, as the expo-
sure of the template representative (as a result of leaving the card) renders it
vulnerable to interception. Across a potentially insecure RF channel, this re-
quires cryptographic protection or the use of digital signatures to secure the
template during communication. Cards used for this process tend to be low
cost state-machines, which presents a challenge to this end. Furthermore, veri-
fication must take place in a device attached to a database, server or network,
which are potential points of vulnerability [21]. This method is therefore not
ideal for use in a secure PACS.
2.9.2 Match on-card
Match on-card (MoC) verification does not require that a template or its repre-
sentative leave the card at any stage, unlike ToC. Instead, the matching stage
takes place within a smart card, without requiring that the stored template
leaves the card.
In this case, a master template is generated during enrollment, as well as
other identifiable information associated with the template, which are stored
on a card instead of a database subsystem. The sensor is located within the
terminal, where a live (query) template or representative thereof is produced
18

22. and passed on to the card, where the matching is algorithm is executed. Conse-
quently any card used in this system requires a microprocessor and an operating
system to invoke the matching algorithm, which may also carry out feature ex-
traction prior to the matching process[44]. Cards used for MoC implementations
are capable of possessing advanced microprocessors and smart card operating
systems that can support this. In addition these operating systems can carry
out authentication, cryptographic and matching operations.
Once the matching process has occurred within the card, a result derived
from the matching process is transferred across the interface between the card
and reader. The security is enhanced in this system, as the tamper resistant
environment of a smart card is used to protect the template and can only be
accessed with a concerted amount of effort and resources available. A useful
example of the kind of architecture used for this process is in [120] which il-
lustrates how a match algorithm stored within the chip (using native code) is
used to process the matching before the result following a successful match is
securely passed up to the application layer.
However, as will be discussed, there are still points of attack that exist in
this system.
2.9.3 System on-card
In a system on-Card (SoC) verification system, the data acquisition, feature
extraction and matching processes all take place within the smart card, as the
fingerprint sensor is present within the card. Cards using this type of verification
system must be capable of high performance and their components are likely to
be expensive.
Potentially the most secure implementation of these strategies is SoC ver-
ification. However, such systems would require investment in state of the art
microcontrollers, including embedded co-processors and additional processing
components [50], which, within a high-volume PACS may not always be cost-
effective. Even CPU chips (processors) up to 32-bit RISC do not fall within
acceptable processing thresholds to cope with performing feature extraction or
image processing computations at least not without significant investment in
additional memory and components to speed up this process [44]. Although
enhancements are certainly progressing in this area and various modified archi-
tectures have been proposed [50], cards used with SoC based implementations
are not widely used, particularly in microprocessing cards with proximity inter-
faces.
This leaves MoC as the most widely accepted form of biometric verifica-
tion that involves the use of a smart card template and a suitable compromise
between capability and security.
19

23. Chapter 3
The Context
The purpose of this chapter to is to discuss the restricted processing capabilities
of PICCs and how this affects the appropriate choice of hardware, software and
additional logical components therein. This will form the basis of a scoped access
control environment, the attacks against which (and their countermeasures) will
be discussed in Chapter 4.
This chapter begins with a discussion of the power and processing constraints
on microprocessing smart cards, particularly those with proximity coupling in-
terfaces (as discussed in section 2.3), and continues with a discussion about how
this affects their performance capabilities. Specifically this will focus on how
these limitations may affect the choice of both hardware and logical components
on the PICC and PCD, and in the latter case the level of feature representation
within an enrolled or live fingerprint template. It is discussed as to how this
ultimately influences the overall physical access control environment and how
the MoC solution provides the best overall compromise between capability, cost,
processing/matching times and security requirements. This will set the scene for
a discussion of a range of application attacks on these constrained environments
and their feasibility, as in Chapter 4.
The following points will be covered in this chapter, within the various sec-
tions:
1. Resource limitations on a smart card in general.
2. Resource limitations on the microcontroller - clock, power, memory and
transmission speed.
3. Resource limitations in data transmission speed.
3.1 Resource Limitations on a Smart Card
Smart cards by their nature are a lot smaller and have restricted capabilities as
compared with conventional computers. A large reason for the limitations in this
area is because of the size specifications influenced (and brought on) both by the
trend in industry and standardisation in the smart card market sector. The ID-
1 cards as described in section 2.2 and with which contactless smart cards must
comply under ISO/IEC 14443, must withstand the respective bending/stress
20

24. tests specified therein, in order that they are suitably flexible to avoid damage,
as per ISO10373-1 [73]; in addition they must adhere to a reasonably small
form factor. This significantly limits the smart card die and capacitor size, and
the specific components included within the smart card microcontroller (i.e.
memory and processor).
3.2 Resources on the Microcontroller
.
A microprocessing smart card will, by its own nature, contain a (sub-electrical
contact) microcontroller, within which the main functional resources are con-
tained. It will contain the following components as a minimum [46]:
• CPU
• Memory: RAM, ROM and EEPROM.
These components are the basic limiting factors with regard the overall per-
formance of a smart card and its ability to perform functions efficiently. The
functions of these components are summarised in the table that follows below.
Resource Function
CPU The processing unit within the microcontroller of a smart
chip. It is responsible for all of the card’s logic and activi-
ties, is closely associated with the memory modules within
the card, and is directly affected by the clock speed (signal).
ROM The memory module that has data written to it once during
the lifetime of a smart card, after which time the contents
become read-only. This data is consistent within a batch of
a production run. In the more advanced smart cards this
contains the elementary parts of an operating system, as
EEPROM can be used to load any additional data or code.
RAM The memory module containing temporary volatile data
used for storage of data (i.e. working memory), used for
run-time processing.
EEPROM The EEPROM is programmable memory, providing the im-
portant task of extending the functional capabilities of a
chip card. This is particularly useful when the smart card
needs to be updated, for example when adding the capa-
bility of a card to stored fingerprint minutiae matching ap-
plication code, as this can be done after the manufacturing
process.
In addition to the latter 3 types of on-board chip memory, flash memory
has been integrated into some of the latest chip cards, as it has faster write
capability, generally ages better than EEPROM and configurations can be soft
loaded. However, there are security concerns about flash memory and its higher
costs, and so at the present time, it is rarely used for verification on-card within
PICCs.
21

25. 3.2.1 Processing Capability and Clock signal
The vast majority of smart cards do not generate their own clock signal, but are
reliant on the clock signal of an external terminal, from which the internal clock
signal (that powers the logic of the microprocessor) is derived. The resultant
internal clock signal of a processor is normally around half that of the external
clock signal generated from the oscillators of a reading device, which further
affects the ability of the processor to perform instructions.
Under the latest revision of the ISO/IEC standard 7816-3 [70], the thresh-
olds for the “VCC” contact of a smart card (compliant with ISO standards)
associated with supply voltage and and supply current are specified for smart
card types A, B and C. The minimum and maximum power supply values spec-
ified are 1.62 and 5.5V and the minimum and maximum supply current values
are specified at 30 and 60 mA respectively.
The standard also specifies thresholds for the CLK contact which receives the
clock signal. The recommended values for when the clock is active is 1MHz and
maximum is 5MHz, although the clock tends to be set at 3.57MHz. [70]. This is
small and becomes a particularly limiting factor under the ID-1 standard card.
This contrasts with the capabilities of state of the art conventional Personal
Computers, which commonly run within the GHz range in respect of clock
signal.
These constraints can be overcome by the use of additional circuitry to
control the internal clock frequency, including phase-lock loops (PLLs )[60].
These are circuits functionally located between the external and internal clocks,
and have been used in embedded systems as a means to boost the frequency of
the internal clock signal [112] derived from that of the external clock (frequency
multiplication). The latest version of ISO/IEC7816 part 3 (2006) accounts for
a maximum clock frequency of 20MHz, which would support this.
In modern smart cards, these components are included: - to resolve issues of
power and clock issues - during the design phase of contact-based smart cards
- for security purposes [59] in biometric-capable embedded systems [11] and as
a method for efficient power management ∗
. However, this is not always ideal,
as clock multipliers can interfere with the clock signal in RF based systems[90],
and do not have any bearing on on EEPROM read/write cycles.
Dedicated crypto-coprocessors have been used to support cryptographic pro-
cessing at a low level, an essential requirement for sufficient security over a con-
tactless interface [36]. The issue of their implementation is no longer considered
one of the important influential factors in terms of the overall processing time
of a smart card[100]. In the current market, RISC architecture-based chips typ-
ically contain these, an example of which is the FameXE coprocessor within the
P5CD036 chip by NXP, [116] which supports advanced cryptosystems.
3.2.2 Memory
The memory modules are all limited in capacity compared to those within PCs.
The ROM, while the most efficient in terms of packing density, allows soft-
ware to be written once-only. Therefore it is typical that the operating system
is loaded into it and remains permanently written therein. It is not used for
∗A PLL can also offset power consumption and regulate clock signal as a consequence of
heavy-duty processing otherwise carried out by any coprocessor with the microcontroller
22

26. any transient storage or dynamic data, although depending on the smart card
ROM mask it can be designed to contain the matching code (during manufac-
turing), in order to reduce the storage burden on the rest of the memory on the
microcontroller. However, adding specialiased functionality to ROM lengthens
the term of the manufacturing process, contributing to increased production
costs. Regardless, with improvements in storage capacity in current generations
of smart cards, (particularly EEPROM) both advanced smart card operating
systems and platforms have been developed that support multiple applications
(and programming interfaces) that extend beyond previous resource limitations.
EEPROM is the next most useful in terms of dynamic storage and overall
packing density. EEPROM does age, however, and is limited to a typical write
cycle threshold of “between 10 000 and 100 000 for EEPROM cells”[48] per
lifetime. EEPROM is important in the context of MoC systems as it is used as
non-volatile long-term storage and is used to store the matching code and/or
reference template of the enrolled user. In microprocessing cards such as the
Java Card, applets that form part of the Java Card API [21] are stored in
EEPROM.
In order to write to EEPROM, the appropriate power supply requires a
write voltage of 12V, although the RF carriers are supplied with 3V and 5V
(as constrained by ISO/IEC 7816). Overall, the power provided by the PCD
to the card is restricted by the electromagnetic spectrum (as specified by ISO
14443) to a value of 7.5 H/m [94]. The extra voltage is provided by a cascaded
charging pump on the microcontroller, which is, as standard, integrated into a
smart card - up to 25V.
RAM is the least densely packed and at a premium compared with the other
memory modules within the microcontroller. This is also a crucial element of
the on-card matching process as it is used to store dynamic, volatile data and
therefore influences the overall runtime environment. This would include any
session data present as well any results required in computations, i.e. matching
results. This is the fastest form of memory to write to (approximately x 1000)
[120]
Historically, and until recently, memory has been a restrictive factor for
smart cards, with a direct bearing on computations, which themeselves require
more complex processing requirements, both in terms of running matching algo-
rithms as well as any cryptographic security mechanisms incorporated. Nonethe-
less, improvements in this space are certainly being seen.
One of the issues that constrained PICCS have (associated with memory)
is that the template sizes held on the RAM are much smaller compared to
the images stored within conventional online databases. As a result biometric
template sizes are short and tend to be around 512 bytes [30] and as such tend
to be transmitted within multiple APDU structures [44].
3.3 Data Transmission
Transmission speeds are naturally among the most influential aspects in terms
of the performance times for on-card verification.
As specified in part 3 of ISO 7816 [70] data at the physical layer are sent
via asynchronous half-duplex connections, whereby bit streams of data are sent.
The equivalent protocols are specified for contactless transmission and are also
23

27. referred to as T=CL [48]. Any aggregated blocks of data are therefore required
to be organised with contingent synchronisation and termination bits flanking
the data. Timing must be coordinated between the reading device and the
smart card, however this process is largely dependent on the clock signal of
the microcontroller. The more commonly deployed 32-bit RISC processors are
being used alongside increased quantities of RAM and EEPROM, such that
this is becoming more increasingly tolerated. In contactless systems, type A
PICCs support higher communication rates up to 848Kbps.[2]. This supports
faster data transfers than the serial interfaces used in many match on-card
implementations.
Within a smart card, the transport and application layer message block
sizes are limited. This is also the case for contactless cards which use the T=CL
protocol, where the APDUs that fit within the frames are bound by an upper
limit of 255 bytes. These limits are again bound by the specifications of the
14443 ISO standard, which has resulted in a fragmented system of transmission,
despite the allowance for chaining of blocks under the message protocol. In
addition, the I/O buffer sizes are limited, which is a significant influential factor
in the timing of communications [85]. In response to this, the buffers can be
increased to tolerate larger message sizes, however this will affect the runtime
environment and demand increased RAM capacity. As RAM is at a premium
in smart cards, even to an extent in current generation microprocessors, this
increases its cost.
3.4 Impact of Constrained Resources
These restrictions all affect the timing in which verification can be performed on
a smart card. What this means in practice, is that extra expense is required in
order to ensure that the each of the microcontoller components are sufficiently
advanced that verification can be carried out within a suitable length of time.
At present time, state-of-the-art specifications do exist, for example, the most
recent version of Gemalto’s .NET card, (which can be utilised with .NET match
on-card application software for match on-card verification) is the .NET v2+
card (chip model SLE88CFX4000P). This card has 400kB EEPROM, 16kB
RAM (and in addition, an internal clock at 66MHz, external clock at up to
10MHz and voltage between 1.52 and 5.5V) [55].
However, this type of advanced processing is still relatively uncommon in
PACS used within large user communities. Each of the hardware components
incorporated at the design phase requires an added level of cost. These costs
will all multiply, when considering the sheer numbers that PACS tolerate. In
practice there are always compromises that are made, even within biometric-
based systems, depending on whether speed of entry is the priority, or the level
of security.
24

28. Chapter 4
Attacks and
Countermeasures
4.1 Introduction
This chapter addresses a range of principle low-cost attacks that may be exe-
cuted against a contactless MoC system incorporating a microprocessing PICC,
biometric reader with a PCD and biometric sensor. This is with respect to a par-
ticular attack profile and generic PACS environment. It also reviews the main
countermeasures against these attacks, before summarising the overall security.
Before these attacks are discussed, the environment will be initially scoped.
This will be done by first outlining a specific class of attacker, which will be
relevant to the overall context of this section. A generic architecture within a
MoC physical access control system (PACS) will then be defined, before a basic
framework for reviewing the points of attack is outlined. This will bring into
focus the main parts of a system that are unique to MoC systems using PICCs,
as distinct from systems where matching is done on a terminal device. Some of
the main attacks within such a system will then be discussed within the scope
of this chapter. Finally the potential countermeasures that can be implemented
to mitigate them will be discussed, and their feasibility evaluated.
In summary, the following topics will be discussed:
• Summary of the generic steps within a Match on-Card PACS, attacker
profile, and the generic environment within scope.
• The types of attack routes on such a system.
• Spoofing attacks on the sensor.
• Attacks across the contactless interface: Replay and Relay attacks.
• Brute force and Hill Climbing Attacks.
• Template protection - transformation functions and biometric cryptosys-
tems.
• An outline of template protection.
25

29. Figure 4.1: A MoC PACS
For ease of reference, the physical access control system as according to the
generic environment discussed, will be referred to as simply the MoC PACS.
4.1.1 The MoC PACS system
Figure 4.1 shows a simplistic outline of the main verification steps to illustrate
how this is used in a contactless MoC system. Broadly speaking the steps are
as follows:
1. The PICC card that contains a minutiae template Y is brought within the
interrogation range of the PCD (not shown in 4.1).
2. The terminal (PCD) and the PICC establish secure communication across
the channel and each is authenticated to the other.
3. A live fingerprint is presented to the fingerprint sensor on the terminal.
4. The scanned fingerprint is processed and the resultant image undergoes
feature extraction and any pre-processing steps, to produce a live minutiae
template Y.
5. The terminal sends the minutiae template to the PICC.
6. The PICC carries out the matching function based on whether X matches
Y and a result (R) of whether the templates are matched is sent (i.e.
R(X=Y?)).
7. The door panel is accessed depending on the result of the matching pro-
cess.
26

30. 4.1.2 The Attacker Profile
In the context of this project, the attacker by definition will be an intelligent
collusive adversary; in other words an adversary with the ability to collude with
personnel at the site of the PACS to gain “insider” knowledge, else an insider
him/her-self. For convenience, the attacker will be frequently referred to as the
profiled attacker .
The equipment available to them will only be of limited cost and sophistica-
tion, but given the attacker’s level of intelligence, they are potentially capable
of creating some additional components of their own, mirroring those of the sys-
tem. This would include rogue cards, external terminal devices and synthetic
gummy fingers, all of which can be covered under a moderate budget.
The focus of the discussion will be held on the main points of vulnerability
inherent in, and unique to a MoC PACS using a contactless interface. As such,
it will exclude any detailed discussions of the generic types of attacks that can
be performed against smart cards (PICCs). The attacks discussed will not focus
on those involving the manipulation or analysis of the PICC, which is mainly
concerned with deriving key information that could be used to compromise
the template stored within. Instead, it will be assumed that the storage of
the template itself is trusted, although in practice this may not be the case.
This is an important exclusion, since a vast number of potential categories of
attack against a card (and therefore the stored template itself) exist as well as
respective countermeasures. Many of these are well covered within [15], [14],
chapter 8.2 of [124] and chapter 9 of [103]. ∗
One reason for this exclusion is that for such an adversary it may very well
be the that they are less likely to dedicate their time and limited resources
on trying to reverse engineer various components of a smart card or analyse
the effect of random power or timing fluctuations, in preference to carrying
out low-cost attacks exploiting very apparent vulnerabilities in such systems.
Consequently, it has been assumed that the attacker’s profile will reflect this,
so that the main attacks of concern can be discussed.
4.1.3 The Generic System
The focus will be refined to reflect the attacker profile described, and a rea-
sonably generic set of components for the PICC and access terminal (reading)
device. This will be used on top of a framework within which various applicable
attacks and countermeasures can be discussed.
In that respect, some assumptions can be made:
• The same basic PACS components as in figure 4.1 will be used.
• The terminal device is assumed to be physically robust, regularly main-
tained and absent of any operational defects.
• The sensor subsystem and feature extraction functions are both contained
within the same subsystems.
• It is assumed that the terminal will not permanently store any card image
or template data, but rather, that it loads a live image into RAM for
∗Although attacks on the storage subsystem are not covered, template security will be
partly addressed in [20]
27

31. the signal processing/feature extraction steps. The RAM will be flushed
periodically as part of normal system housekeeping.
• Any components used as part of access control decision making (as per
2.1) are assumed to be secure.
• The PICC will store and match ISO 17974-2 format minutiae templates.
• The type of fingerprint sensor shall not be pre-defined explicitly - it is
assumed to be a reasonably middle-market optical or capacitative sensor.
• The terminal device is free of any malware and on a hardened network.
The component-level features of the system shall not be further defined.
4.2 Applicable attack routes
Jain et al [80] categorised two broad categories of failure type that can be desig-
nated to a biometric system. The first is an“intrinsic failure”and the second a
system failure, as a result of an adversarial attack. It has already been assumed
that the terminal is absent of operational defects. In other words, the system
is assumed to operate within its intended parameters. Hence intrinsic failures
are not of concern in the scope of this discussion. However, this is not to pre-
clude the possibility that attackers can exploit various weaknesses inherent in
terminals (in particular their sensors), which is of course the principle topic of
focus.
Within a standard biometric verification system, there are various points of
compromise that can be realised. In [126], 8 distinct points of compromise are
highlighted, applicable to such generic biometric systems. This concerns the
following points of attack:
1. At the point of the sensor - i.e. by presentation of a fake biometric.
2. Along the communications interface between the sensor and feature ex-
traction subsystems.
3. Within the feature extraction subsystem.
4. Between the feature extraction subsystem and matching subsystems.
5. Within the matching subsystem.
6. Within a template storage subsystem, at the level of the stored template.
7. Along the channel between the storage and matching subsystems.
8. Between the matching subsystem and application device.
This is useful, to an extent, as a framework for discussion. However, if we
map this onto the model representation of the MoC PACS as in figure 4.1, some
of these attacks are not applicable because of the different logical locations of
the subsystems concerned and because of the specific scope of this project.
Attack point 3 involves overriding the feature extractor, which would theo-
retically involve the use of malicious software (to both override the feature set
28

32. and select arbitrary features within the system). The system could then be later
compromised. Cleverly crafted software, including Trojan Horses would allow
an attacker to willingly inject variations to do so. However, the infrastructure
concerning the attacks in focus incorporates a hardened network.
As the interface between the sensor and the feature extraction subsystem
is contained within the terminal device, any replay attacks between the sensor
and feature extraction subsystems (attack point 2) are out of scope. It is also
assumed that the sensing and feature extraction functions are held within the
same subsystem, which would exclude attack number 2 from consideration. As
discussed in 4.1.2 it is assumed that the smart card is trusted.
Hence the applicable attack points as within this model are 1, 2, 4 and 8,
and will all be discussed in the following sections.
In addition to the inapplicability of some of these attack points, it is worth
noting that attack point 4 occurs across the contactless interface in this MoC
implementation. The same interface would be used in a further attack point
between the matching subsystem and the decision making subsystem (attack
point 8).
4.3 Spoofing attacks on the sensor
The sensor subsystem (attack point 1) is still a major point of vulnerability
within a biometric system and there are various potential attacks that can be
targeted against it within a MoC system.
A fingerprint sensor is a device responsible for reading the surface character-
istics of a finger, in particular ridges and valleys. The vast majority of these fall
into one of two categories - optical or solid state, with the most common of the
latter (and most widely used sensors) being capacitative sensors. The optical
sensors generally detect reflective differentials, and capacitative sensors elec-
tronic transitions (capacitance) between valleys and friction ridges[141]. Both
of these types are commonly used among MoC systems, for example the capac-
itative sensor used by Precise Biometric’s BioAccess 200 fingerprint scanner[24]
and the optical sensor within the MorphoAccess 120 PIV card [130]. Several
examples of both types of sensor are given in Chapter 2 of [79].
The most simplistic actually require no intervention from an impostor, but
instead the use of pre-existing latent fingerprints. Among the attacks of this
kind, early attacks on these sensors were observed as documented within [93],
whereby fingerprint reading devices with capacitative sensors (albeit on a desk-
top mouse) were fooled by the simple act of breathing on the front of the sen-
sor, assisted by the act of hand cupping around it. On the basis that there
was sufficient fatty residue left behind this attack was shown to be effective in
reactivating the latent fingerprint to fool the system.
It has also been shown, as documented as in [45], that attacks can be carried
out by developing latent fingerprints (using printing toner) and then lifting them
with tape, as well as producing wax moulds to use against a sensor.
A ground-breaking study from Matsumoto et al [102] observed that artificial
fingerprints can synthesised from gelatinous “gummy” sheaths designed to fit
around fingertips of impostors. In these cases the artificial fingers fooled 11
state of the art sensors, both of the optical or capacitative categories.
29

33. 4.3.1 Anti-spoofing countermeasures and exploitability
There are a great variety of liveness detection mechanisms that have been de-
veloped and are available in sensors, the main types of which will be covered.
As the vast majority of sensors are optical and solid-state, most of the spoof-
ing mechanisms relate to the liveness detection mechanisms built within these
types. This includes the generic environment in focus. Other types of sensor do
exist, including ultrasonic sensors, that use high frequency signals and resultant
echo signals from a fingerprint layer. An example includes using high frequency
ultrasonic pulses reflecting off the fingerprint surface, which measure acoustic
impedance between surface features and the valleys (i.e. air) to produce an im-
age of the fingerprint [134]. However these components are currently reasonably
expensive, hence the focus will be on the former types of sensor.
Various studies on liveness detection have tried to address exactly what
vulnerabilities artificial fingerprints exploit, and the fingerprint properties that
these relate to. A useful categorisation [52] summarises 3 major categories of
these property:
• Analysis of skin details.
• Static properties of the finger i.e. temperature.
• Dynamic properties of a finger.
The various types of detection mechanisms as will be discussed, relate to
these categories.
Skin Details:
The coarseness of the skin surface of a finger can be detected, and used, to
differentiate between a live and artificial finger, as the latter is general more
coarse. In [107] this was done by treating the coarseness as white noise relative
to the ridge features, and removing it using wavelets. Other features of the
skin have been measured including sweat pores, which can be detected at high
resolution. This was needed because of proven studies to show that these could
be reproduced easily in such artificial fingers [102].
Static Properties of the Finger:
Capacitance and reflective characteristics, as described, are also in this cat-
egory, and by using the attacks methods as described in 4.3 these can also be
fooled by latent lifts and artificial moulds or gelatinous fingerprints of various
sorts. In the former case, the capacitance is simply removed from the equation
by adding saliva or water to reduce conductivity, which can fool the system. In
the latter, optical sensors (which measure reflective light) can also be fooled by
gelatinous artificial fingers with a similar composition of an artificial finger, or
a thin silicone layer, which will display similar optical properties to that of an
enrolled user’s finger [79].
The thermal properties of the finger are another type of static property fac-
tored into simple liveness detection mechanisms, on the basis that temperatures
within gelatinous fingers would normally be a couple of degrees cooler than live
finger ambient temperatures. Solid state scanners will detect temperature and
verify the finger according to the temperature it is preset at [151]. However,
fingerprints suffer irregularities not just in body temperature, which can vary
to a small degree, but also from outside influences. Moreover, the differences
30

34. between live and artificial fingerprints are small and so it becomes difficult (and
counterproductive) to limit verification according to any meaningful tempera-
ture range. As a result, artificial fingers and gelatinous sheets will fool several
of these detection mechanisms, the former of which can be incrementally heated
up in a plastic bag and the latter simply placed on a finger until it is verified
[93].
Dynamic Properties of a Finger:
Probably the most effective of all detection mechanisms rely on those char-
acteristics that are unique among fingerprints, i.e. those that vary and are
dynamic. There are several types of dynamic characteristic and they will not
be covered exhaustively.
Blood pressure and pulse oximetry (oxygenation of haemaglobin) can be de-
tected by optical scanners, which are augmented to image fingerprint subdermal
layers on the basis of several characteristics. An example of this is chromatic
texture, as visible under different wavelengths, to differentiate between spoofed
and live fingers [114].
More recently, multispectal analysis has been extended so that other meth-
ods, such as contactless imaging with polarisation, are combined [9].
Skin elasticity is another aspect that can be used to distinguish finger types,
and can be used to create a unique image, when (for example) they are com-
pressed and rotated against a sensor [16].
Another dynamic feature that is commonly exploited for use within novel
detection mechanisms is electronic odour, whereby the emission of odourants is
detected and utilised to profile a fingerprint. Electronic noses have been built
which can detect such emmisions. [19]
4.3.2 Feasibility of Spoofing Attacks
As per the broad variety of attacks and defenses described above, the question of
how effective these attacks and countermeasure can be, depends on the relative
level of resources both on the part of the system owners and the attacker. With
only a moderate amount of investment, it is a given that some basic liveness
detection mechanisms will be included, and will stop the attacks that use latent
fingerprints or moulded fingerprints. Indeed this is the case with most current
implementations of MoC. This is with the exception of artificial fingers that
can be developed from them, as in [131]. This attack is potentially one of
the more serious as it can be available to the profiled attacker without any
involvement from an enrolled user. In general, the liveness detection mechanisms
that observe the details of the skin and static properties of the finger, are prone
to spoofing. Given enough time, the resources available to the profile attacker
will counter such measures with reasonable ease.
Some recent methods have been proposed to detect static features by sta-
tistical analysis and fusion of their results [33]. However, whether building-in
these detections is cost-beneficial/scalable or not, is questionable. Spoof detec-
tion using the dynamic features of a fingerprint is generally more effective than
the other methods, but with any new implementation, it can also be expensive.
In addition, one major hurdle to the operation of effective liveness detection
controls, is the usability of the system. Those sensors detecting elasticity, such as
the one described previously, would in practice involve a certain level of training.
Moreover, they would become prone to FTE errors(as in 2.7) because they are
31

35. sensitive to the way in which the subject places or rotates their finger. This is
one major reason why many of the current fingerprint scanners on the market
still use semiconductor technology, which itself gives premise to the fact that
some may still be fooled by the methods described. It is also worth mentioning
that there is always an issue facing those administrating these systems, in terms
of the optimal tolerance thresholds (EER levels) for these systems. If the FAR
tolerance is set too low there is a higher chance of false rejection errors occurring,
which would impact negatively on the principle operation of a PACS system -
access throughput. Naturally, where the opposite is the case, it is more probable
that an impostor using one of the above spoofing attacks will be successful, and
there will be a false acceptance.
The choice of spoof detection is therefore of paramount importance in a MoC
system and although perhaps a “swiss cheese” model that blends several types
together may be preferable[95], this may lengthen the entire verification process
to the detriment of the system’s usability.
4.4 Attacks across the Contactless Interface
One of the main concerns within the MoC PACS is that a contactless channel
may be compromised. There are a number of feasible attacks that may exploit
the fact that within the MoC PACS a contactless communication channel is
being used. As it is the case that a type A PICC is being used, there are a
variety of these.
4.4.1 Replay Attack
If the communications channel can be intercepted, one attack potentially avail-
able to the profiled attacker is a replay attack (points 4 and 8 as per 4.2). In
this case, either of the transmissions between the terminal (PCD) or the PICC
(or vice versa) could be directed in near real-time to the other. For this at-
tack to be successful it is a requisite that the attacker possesses a rogue PICC
and a modified, rogue terminal. Once the communication channel has been
eavesdropped, then the same message can be re-transmitted back to the MoC
system. Assuming that an attacker is successful, they could potentially use this
to feed back a successful match score whether it is one that has been sent clear
or transformed (as discussed in 4.7).
Simple replay attacks have been long understood and can be countered by
a variety of cryptographic protocols [113] which build in random challenges,
nonces and timestamps (challenge-response) as measures of freshness. These
traditional measures will not be discussed at granular protocol level, but for
reference the reader should see [91],[58][104].
One issue apparent within biometric verification systems, is that traditional
methods of freshness detection and challenge-response cannot apply to biometric
templates; they rely principally on a challenge being sent and the response
being some measure of calculation or transformation functions. As a result,
there would be no measure of freshness, because it would be based solely on
the performance of the functions performing that calculation [28]. A difference
exists where passwords and tokens are concerned, in that erroneous passwords
are more easily detectable, and more easily defended against replay attacks,
32

36. because they cause no variation in signal that may be exploited (replayed). The
opposite is the case for biometrics, where the signal varies. This also has an
impact on the feasibility of brute-force attacks as discussed in 4.5.
As a result, modified protocols are used in such systems. The challege-
response protocol as in [28] factors in both the content of the biometric template
and the varying content of a challenge, before the feature extraction stage. This
is illustrated in figure 6.1 (see 6.4) and shows that a transformation function
takes both of these as input. This provides a measure of freshness in relation to
when the challenge was sent. Therefore the content cannot simply be replayed
from the communication channel without an attacker having knowledge of the
challenge-response function (f ). As the security of this protocol depends on
the randomness of the function, the transfomation function (and the random
number generator) should not be easily predictable.
Some insecure implementations exist, whereby weak challenge-response mea-
sures have been used. A pertinent example is the MiFare Classic protocol [115],
which was attacked successfully after the crypto-1 algorithm was reverse engi-
neered. This was as a result of a weak random number generator [35], [89].
This is a concern as some proximity match on-cards still use this proprietary
algorithm [25] and obviously highlight this above point.
4.4.2 Relay Attacks and Countermeasures
Irrespective of any challenge-response protocol used, relay attacks are a very
effective method for bypassing any such controls and attacking the communi-
cation channel. This is a man-in-the-middle type attack where the attacker
is in possession of a modified reader and/or PICC card and manipulates the
communication channel between the PCD and the PICC. Such an attack, as
demonstrated in [63] can be directed at (type A) 14443 compliant cards. This
attack involves enhancing the signal range beyond the specified range of 10cm
†
and up to 50cm, using a modified terminal, often accounted for by the ad-
dition of a larger aerial size. A standard man-in-the-middle attack would be
otherwise difficult to affect within this range, whereas this method offers better
opportunity for discretion and would therefore not be limited to crowded areas.
There are inherent delays in performing such an attack and these would be
potentially noticeable within the overall context of a MoC verification transac-
tion. This is one aspect that is addressed by one of the more effective types
of countermeasure. The earliest of the notional differential timing countermea-
sures introduced the concept that a challenge-response protocol could incorpo-
rate an upper bound set on the time, which could be applied to public key
implementation [29]. Since then, various protocols incorporating this type of
countermeasure have been proposed and assessed.
One of the first countermeasures to incorporate the above idea was demon-
strated in [64] whereby a single round protocol was designed on the basis of a
similar challenge-response protocol as above, but modified so that time for the
response was measured in reference to the speed of light. This was recognised
as a good measure of freshness, by inference from the distance of the reader,
but this countermeasure did not address the problem of entity authentication
of the reader to the PICC, and assumed both to be rogue. In [129] this issue
†see 2.5
33

37. was addressed after recognising that the “proover” (genuine PCD) could be
used in collusion with a rogue verifier (card) and so if a shared secret is dis-
closed, it could defeat the protocol. The proposed protocol builds upon previous
challenge-response protocols and instead uses pseudoramdom keys generated by
a MAC, and the encryption of a longterm shared secret (with a one time pad)
using pseudorandom bits. In practice, this level of randomness built on top of
previous distance-bound protocols will help to expose the presence of the PCD
and PICC.
In general, these can be used as effective mechanisms to detect replay at-
tacks. However, one of the problems faced by MoC PACS system owners is that
noise across the contactless interface potentially degrades the efficacy of the
countermeasure [106]. The reason for this can be attributed to latency, which
in turn can hinder the performance of a relay attack. It is also of significant note
that the effect of latency may be exacerbated by the limits bound by both the
ISO 14443 standard in terms of transmission speed and clock, and the proximity
range accounted for (discussed in 3.2.1 and 3.3). Further studies may focus on
attacks that take further advantage of noise and suitable countermeasures.
To the profiled attacker, if successful, this would mean that (encrypted)
template data could be transmitted from a genuine PCD to a rogue PICC, and
a resultant match response sent to the rogue PICC card to provide verification
at the point of access. Albeit, to carry this out successfully might be practically
challenging for the profiled attacker, because it may seem obvious to a genuine
card holder that they have bypassed the requirement to be physically present
at the fingerprint scanner. This is the case if they attempt to access the same
portal. On the other hand, if there is another portal within the interrogation
range, this becomes a trivial matter. As the profiled attacker is capable of
coercing or convincing them to become rogue at any point, access may also be
facilitated with their assistance.
As is often the situation, it can become a case of cat and mouse between
those employing security controls and those exploiting their vulnerabilities. At
the very least, the administrators of the system should keep up to date with
all versions of the API, and the API should support a range of functions ‡
as
well as a robust security management framework. This is both the case for the
protection against replay and relay attacks.
4.5 Brute Force Attacks
.
Brute force attacks within biometric systems are potentially possible. In
other words, all combinations of a biometric can be tried out until there is a
match. This would technically confirm a point of attack at the sensor (attack
point 1) or at point 3. This is assuming that a Trojan horse is present, whereas
(in fact) in the MoC PACS system this is not the case. This particular attack
point will therefore be excluded. This would leave only the first attack point
available for a real-time attack, which would be difficult within the MoC PACS
environment as it is monitored.
Taking into account the exact profile of the (“profiled”) attacker, a brute
force attack will be discussed, as the insider element makes this possible. With
‡for example those that are compliant with the BioAPI standard as in [5]
34

38. some insider assistance or a modified terminal device, an attacker may be able
to brute force attack the template using the sensor and some feedback about
whether the result has been successful. If the attacker has access to the match-
ing score and the matching process is not protected sufficiently, then this is
possible. Brute force attacks may also be peformed at attack point 4, between
the feature extractor and the matcher if insufficient template protection meth-
ods are used (these will be discussed in 4.7). As already stated in 4.4.1, there is
variability within a biometric signal [28], which means that brute force attacks
on biometric templates are difficult to detect. This is largely because they are
significantly longer and more complex and opposed to linear bit string values
that are typically of negligible size to attack [126]. Many of the minutiae fea-
tures are correlated, and this is often factored into a transformation function,
in which form the template is typically sent. These properties may be exploited
by an attacker whom will have the opportunity to use several databases of arti-
ficial templates to store any detected combinations in a dictionary based attack.
[148]. §
.
Some efforts have been made to understand the complexities of brute force
attacks. As in [28], estimations were made of the probabilities of random genera-
tion of minutiae points that correlate with those as within an enrolled template.
Based on the formulations used, some useful deductions were made in relation
to the security of a template:
1. (1) The security of a template depends on 3 variables that are related -
the number of independent feature points (such as independent spatial
locations of minutiae), number of independent locations and the absolute
number of minutiae.
2. (2) The higher the amount of feature-level information and the lower the
number of minutiae, the higher the security.
In terms of the latter, this is particularly the case if erroneous minutiae are
removed [126].
This emphasises that it should be considered carefully, when designing a
(transformed) template resistant to brute force attacks, that the template should
be error free and furthermore, any template matching function should be de-
signed to exhibit low tolerance to anomalous minutiae. This latter point very
much ties in with how well a template is protected and the transformation
functions used for matching, which will be discussed in 4.7.
4.6 Hill Climbing Attacks
An additional attack that can be performed at the same attack points as for
replay attacks, is a Hill Climbing Attack., which makes use of the feedback
provided by a matching score, to enhance each of the subsequential attempts.
[101]. Within a MoC PACS this would occur in the back channel between
the PICC and PCD. This is where various iterations of input data are made
on the basis of feedback from the result of previous data sent and subsequent
modifications. To launch such an attack, the attacker would need to know
§this specific aspect is covered in 4.9.2
35

39. something about the input image size and template format in advance. The
template size itself could, of course, be published by the vendor as is sometimes
the case [101].
In the case of the profiled attacker, they may not have the capability to
perform attacks on the hardware of the smart card itself and therefore derive the
long-term template encryption key (or template data) from the stored template.
Equally they may not have direct access to any cryptographic keys that secure
the transmission of the match results back to the access control subsystem.
However, if any of this key information was discovered by collusive measures
with an insider, this attack becomes feasible. This is therefore another possible
attack for the profiled attacker.
The first major example of this, as applicable to fingerprints, was as in
[137]. This demonstrated that by using artificially generated templates and
feedback from the result score, various subsequent permutations can performed
at the pixel level. Eventually feedback obtained from the result revealed where
a match threshold had been exceeded and showed that the probability of doing
so significantly increases where a previous change has already raised the score.
Further progression of this was made in [150] and [101] which extended the
main principles of this technique. In the first of these, standard minutiae formats
were used (in line with those of [6]), using physical coordinates and orientation.
The set up was as in figure 4.2.
Figure 4.2: Hill Climbing Attack System [150]
¶
The method that was demonstrated involved making use of various changes -
pertubing, adding, replacing or removing existing minutiae, and acquiring those
producing the best matching score to be use for further rounds. This can be
36

40. done successively using such a technique, until the matching score is known.
The results of this were that for a FAR rate set at the standard of 0.1% the
probability of a successful attack was reduced significantly from the expected
probability (1 in 1000).
The second of these approaches was significant in that it adopted much of
the previous approach to test a scenario with a MoC implementation. The test
methodology used a minutiae database of 100 randomly generated synthetic
templates and acquired the mean number of minutia, from which a 9 by 9 cell
of pixels was made. 4 significant types of successive modification were made by:
1. moving a minutiae to a neighbouring cell.
2. addition another minutia
3. substitution of a minutia
4. removal of the minutia.
The results obtained showed that of these method types, 2 and 3 were partic-
ularly effective, and there were significantly higher success rates against a MoC
implementation. This was an indicator that where lower number of minutiae
are present, as within a PICC, this type of approach is more effective. Of course
this relates back again to the overall constraints within a smart card. At the
present time, this is still of consequence because minutiae templates are around
the 512 bytes mark, meaning that verification in a MoC system is more prone
to this type of attack. It was also shown to be functionally less complex than
adopting a brute force attack, for which all permutations would need to be run.
As suggested by [137] the main approach used to counter these attacks is
to output only a quantised result, which would consequently reveal little about
the effectiveness of a permutation, on the basis that minor changes to template
(minutiae) will not change the output match score. However, it has been shown
in [10] that despite the implementation of quantisation as specified, Hill Climb-
ing attacks can be successfully implemented if noise is added. This idea was
conceived on the basis that if the spacing within the quantisation is too wide
(promoted by the addition of noise), then the verifying software may not be
capable of interpreting the results one way or another. Within the results of the
experiment, this enabled sufficient levels of scoring output, in order that several
facial images could be constructed. This is also possible, as within the context
of fingerprint minutiae.
Obviously there is a requirement on the part of an attacker, that they are
aware of any (biometric) API used on a PICC with a platform capable of in-
terfacing with one as this will be needed to manipulate any protocol using
quantisation. If the API is not publicly known, they would rely on some other
way of possessing knowledge of the protocol. For the profiled attacker that may
be done by one of the methods already described.
4.6.1 Injection Attacks
Intercepting communications from the PICC to the PCD could be used to derive
information about the enrolled template and conceivably reconstruct part of a
Java BioAPI for example
37