The Biometric Dilemma

S E C U R E

C O M P U T I N G

July 2002

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R. Smith
-

Biometric Dilemma

The Biometric Dilemma

Rick Smith, Ph.D., CISSP

rick_smith@securecomputing.com

28 October 2001

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Biometric Dilemma

Outline

•
Biometrics: Why, How, How Strong

–
Attacks, FAR, FRR, Resisting trial
-
and
-
error

•
Server
-
based Biometrics

•
Attacking a biometric server

–
Digital spoofing, privacy intrusion, latent print reactivation

•
Token
-
based Biometrics

•
Physical spoofing

–
Voluntary and involuntary spoofing

•
Summary

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Biometric Dilemma

Biometrics: Why?

•
Eliminate memorization
–


–
Users don’t have to memorize features of their voice, face,
eyes, or fingerprints

•
Eliminate misplaced tokens
–


–
Users won’t forget to bring fingerprints to work

•
Can’t be delegated
–


–
Users can’t lend fingers or faces to someone else

•
Often unique
–


–
Save money and maintain database integrity by eliminating
duplicate enrollments

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Biometric Dilemma

The Dilemma

They always look stronger and and easier to use
than they are in practice


•
Enrollment is difficult

–
Easy enrollment = unreliable authentication

–
Measures to prevent digital spoofing make even more work for
administrators, almost a “double enrollment” process


•
Physical spoofing is easier than we’d like

–
Recent examples with fingerprint scanners, face scanners

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Biometric Dilemma

Biometrics: How?

Measure a physical trait


•
The user’s fingerprint,
hand, eye, face

Measure user behavior


•
The user’s voice, written
signature, or keystrokes

From
Authentication

© 2002. Used by permission

From
Authentication

© 2002. Used by permission

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Biometric Dilemma

Biometrics: How Strong?

Three types of attacks

•
Trial
-
and
-
error attack

–
Classic way of measuring biometric strength

•
Digital spoofing

–
Transmit a digital pattern that mimics that of a legitimate
user’s biometric signature

–
Similar to password sniffing and replay

–
Biometrics can’t prevent such attacks by themselves

•
Physical spoofing

–
Present a biometric sensor with an image that mimics the
appearance of a legitimate user

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Biometric Dilemma

Biometric Trial
-
and
-
Error

How many trials are needed to achieve a 50
-
50
chance of producing a matching reading?


•
Typical objective: 1 in 1,000,000


2
19


•
Some systems achieve this, but most aren’t
that accurate in practical settings


•
Team
-
based attack

–
A group of individuals take turns pretending to be a legitimate
user (5 people X 10 finger = 50 fingers)

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Biometric Dilemma

Passwords: A Baseline



Example

Type of
Attack

Average
Attack
Space

Random 8
-
character
Unix password

Interactive
or Off
-
Line

2
45

Dictionary Attack

Interactive
or Off
-
Line

2
15
to 2
23

Mouse Pad Search

Interactive

2
1
to 2
4

Worst Case


2
1



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Biometric Dilemma

Biometric Authentication

•
Compares user’s
signature

to previously
established
pattern

built from that trait

•
“Biometric pattern” file instead of password file

•
Matching is
always

approximate,
never

exact

From
Authentication

© 2002. Used by permission

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Biometric Dilemma

Pattern Matching

We compare how closely a signature matches
one user’s pattern versus another’s pattern

From
Authentication

© 2002. Used by permission

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Biometric Dilemma

Matching Self vs. Others



From
Authentication

© 2002. Used by permission

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Biometric Dilemma

Matching in Practice

FAR = recognized Bob instead; FRR = doesn’t recognize me

From
Authentication

© 2002. Used by permission

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Biometric Dilemma

Measurement Trade
-
Offs

We must balance the FAR and the FRR

•
Lower FAR = Fewer successful attacks

–
Less tolerant of close matches by attackers

–
Also less tolerant of authentic matches

–
Therefore
–

increases the FRR

•
Lower FRR = Easier to use

–
Recognizes a legitimate user the first time

–
More tolerant of poor matches

–
Also more tolerant of matches by attackers

–
Therefore
–

increases the FAR

Equal error rate = point where FAR = FAR

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Biometric Dilemma

Trial and Error in Practice



Example

Type of
Attack

Average
Attack
Space

Biometric with 1% FAR

Team

2
6

Biometric with 0.01% FAR

Team

2
12

Biometric with “One in a million”

Team

2
1
9



•
Higher security means more mistakes

–
When we reduce the FAR, we increase the FRR

–
More picky about signatures from legitimate users, too

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Biometric Dilemma

Biometric Enrollment

•
How it works

–
User provides one or more biometric readings

–
The system converts each reading into a signature

–
The system constructs the pattern from those signatures

•
Problems with biometric enrollment

–
It’s hard to reliably “pre
-
enroll” users

–
Users must provide biometric readings interactively

•
Accuracy is time consuming

–
Take trial readings, build tentative patterns, try them out

–
Take more readings to refine patterns

–
Higher accuracy requires more trial readings

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Biometric Dilemma

Compare with Password or
Token Enrollment

•
Modern systems allow users to self
-
enroll

–
User enters some personal authentication information

–
Establish a user name

–
Establish a password: system generated or user chosen

–
Establish a token: enter its serial number

•
Password enrollment is comparatively simple

•
Tokens require a database associating serial
numbers with individual authentication tokens

–
Database is generated by token’s manufacturer

–
Enrollment system uses it to establish user account

–
Token’s PIN is managed by the end user

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Biometric Dilemma

Biometric Privacy

•
The biometric pattern acts like a password

But biometrics are
not

secrets


•
Each user leaves artifacts of her voice,
fingerprints, and appearance wherever she
goes

•
Users can’t change biometrics if someone
makes a copy

•
We can trace people by following their
biometrics as they’re saved in databases

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Biometric Dilemma

Server
-
based biometrics

•
Boring but important

•
Some biometric systems require servers

–
When you need a central repository

–
Identification systems (FBI’s AFIS)

–
Uniqueness systems (community social service orgs)

From
Authentication

© 2002. Used by permission

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Biometric Dilemma

Attacking Server Biometrics



From
Authentication

© 2002. Used by permission

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Biometric Dilemma

Attacks on Server Traffic

•
Attack on privacy of a user’s biometrics

–
Defense = encryption while traversing the network

•
Attack by spoofing a digital biometric reading

–
Defense = authenticating legitimate biometric readers

Both solutions rely on trusted biometric readers

From
Authentication

© 2002. Used by permission

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Biometric Dilemma

Trusted Biometric Reader

•
Blocks either type of attack on server traffic

•
Security objective
–

reliable data collection

•
Must embed a cryptographic secret in every
trusted reader

–
Increased development cost

–
Increased administrative cost
–

administrators must keep the
reader’s keys safe and up
-
to
-
date

•
Must enroll both users
and

trusted readers

–
“Double enrollment”

–
Database of device keys from biometric vendor

–
One device per workstation is often like one per user

–
Standard tokens are traditionally lower
-
cost devices

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Biometric Dilemma

Another Server Attack

•
Experiments in the US and Germany

•
Willis and Lee of
Network Computing

Labs, 1998

–
Reported in “Six Biometric Devices Point The Finger At Security” in
Network Computing
, 1 June 1998

•
Thalheim, Krissler, and Ziegler,
2002

–
Reported in “Body Check,”
C’T

(Germany)

–
http://www.heise.de/ct/english/02/11/114/

•
Attack on “capacitive” fingerprint sensors

–
Measures change in capacitance due to presence or absence of
material with skin
-
like response

–
65Kb sensor collects ~20 minutiae from fingerprint

–
Traditional techniques use 10
-
12 for identification

•
Attack exploits the fatty oils left over from the last
user logon

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Biometric Dilemma

Latent Finger Reactivation

•
Three techniques

–
Oil vs. non
-
oil regions return difference as humidity increases

1.
Breathe on the sensor (Thalheim, et al)

–
You can watch the print reappear as a biometric image

–
Works occasionally

2.
Use a thin
-
walled plastic bag of warm water

•
More effective, but not 100%

–
Works occasionally even when system is set to maximum sensitivity

3.
Dust with graphite (Willis et al; Thalheim et al)

•
Attach clear tape to the dust

–
Press down on the sensor

–
Most reliable technique
–

almost 100% success rate (Thalheim)

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Biometric Dilemma

This Shouldn’t Work

•
According to Siemens
–

vendor of the

“ID Mouse” used in those examples
–


–
Authentication procedure remembers the last fingerprint used

–
System rejects a match that’s “too close” to the last reading
as well as a match that’s “too far” from the pattern

•
Observations

1.
Defense didn’t work in these experiments

2.
Tape can be repositioned to create a ‘different’ reading

3.
Hard to track through multiple biometric readers

–
Assume the user logs in at multiple locations over time

–
Then the latent image on some reader is
not

the most
recent one accepted for login

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Biometric Dilemma

What about “Active”
Biometric Authentication?

•
Some (Dorothy Denning) suggest the use of biometrics
in which the pattern incorporates “dynamic”
information uniquely associated with the user

•
Possible techniques

–
Require any sort of non
-
static input that matches the built
-
in pattern

•
Moving the finger around on the fingerprint reader

–
Challenge response that demands an unpredictable reply

•
Voice recognition that demands reciting an unpredictable phrase

•
Both are vulnerable to a dynamic digital attack based
on a copy of the user’s biometric pattern

•
Ease of use issue

–
Requires more complex user behavior, which makes it harder to use
and less reliable

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Biometric Dilemma

Attacking Active Biometrics

A feasible dynamic attack uses the system’s algorithms

to generate an acceptable signature

•
Example

–
Attacker collects enough biometric samples from the victim to build a
plausible copy of victim’s biometric pattern

–
During login, attacker is prompted for a spoken phrase from the victim

–
Attack software generates a digital message based on the user’s
biometric pattern

•
There may be a sequence of timed messages or a single message
–

it doesn’t matter

If the server can predict what the answer should be,
based on a static biometric pattern, so can the attacker

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Biometric Dilemma

Token
-
Based Biometrics

Authenticate with biometric + embedded secret

From
Authentication

© 2002. Used by permission

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Biometric Dilemma

Token Technology

•
Resist copying and other attacks by storing the
authentication secret in a tamper
-
resistant package.

From
Authentication

© 2002. Used by permission

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Biometric Dilemma

Tokens Resist

Trial
-
and
-
Error Attacks



Example

Type of
Attack

Average
Attack
Space

Reusable Passwords

Interactive
or Off
-
Line

2
1
to 2
45

Biometrics

Team

2
6
to 2
19

One
-
Time Password Tokens

Interactive
or Off
-
Line

2
19
to 2
63

Public Key
Tokens

Off
-
Line

2
63
to 2
116



These numbers assume that the attacker

has
not

managed to steal a token

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Biometric Dilemma

Biometric Token Operation

•
The “real” authentication is based on a secret
embedded in the token

•
The biometric reading simply “unlocks” that
secret

•
Benefits

–
User retains control of own biometric pattern

–
Biometric signatures don’t traverse networks

•
Problems

–
Biometric Tokens cost more

–
Less space and cost for the biometric reader

The biometric serves as a PIN

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Biometric Dilemma

Attacks on Biometric Tokens

•
If you can trick the reader, you can probably
trick the token

•
Digital spoofing shouldn’t work

–
We’ve eliminated the vulnerable data path

•
Latent print reactivation (remember?)

–
Tokens should be able to detect and reject such attacks


•
Attacks by cloning the biometric artifact

–
Voluntary cloning (the authorized user is an accomplice)

–
Involuntary cloning (the authorized user is unaware)

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Biometric Dilemma

Voluntary finger cloning

1.
Select the casting material

–
Option: softened, free molding plastic (used by Matsumoto)

–
Option: part of a large, soft wax candle (used by Willis; Thalheim)

2.
Push the fingertip into the soft material

3.
Let material harden

4.
Select the finger cloning material

•
Option: gelatin (“gummy fingers” used by Matsumoto)

•
Option: silicone (used by Willis; Thalheim)

5.
Pour a layer of cloning material into the mold

6.
Let the clone harden

You’re Done!

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Biometric Dilemma

Matsumoto’s Technique

•
Only a few dollars’ worth of materials

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Biometric Dilemma

Making the Actual Clone

You can place the “gummy finger” over your real finger.
Observers aren’t likely to detect it when you use it on a
fingerprint reader. (Matsumoto)

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Biometric Dilemma

Involuntary Cloning

•
The stuff of Hollywood
–

three examples

–
Sneakers

(1992) “My voice is my password”

–
Never Say Never Again

(1983) cloned retina

–
Charlie’s Angels

(2000)

•
Fingerprints from beer bottles

•
Eye scan from oom
-
pah laser

•
You clone the biometric without victim’s
knowledge or intentional assistance

•
Bad news: it works!

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Biometric Dilemma

Cloned Face

•
More work by

Thalheim, Krissler, and Ziegler

•
Reported in “Body Check,” C’T (Germany)

http://www.heise.de/ct/english/02/11/114/

•
Show the camera a photograph or video clip
instead of the real face

–
Video clip required to defeat “dynamic” biometric checks

•
Photo was taken without the victim’s
assistance (video possible, too)

•
Face recognition was fooled

–
Cognitec's FaceVACS
-
Logon using the recommended Philips's
ToUcam PCVC 740K camera

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Biometric Dilemma

Matsumoto’s 2
nd

Technique

Cloning a fingerprint from a
latent

print


1.
Capture clean, complete fingerprint on a glass, CD,
or other smooth, clean surface

2.
Pick it up using tape and graphite

3.
Scan it into a computer at high resoultion

4.
Enhance the fingerprint image

5.
Etch it onto printed circuit board (PCB) material

6.
Use the PCB as a mold for a “gummy finger”

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Biometric Dilemma

Making a Gummy Finger
from a Latent Print

From Matsumoto, ITU
-
T Workshop

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Biometric Dilemma

The Latent Print Dilemma

•
Tokens tend to be smooth objects of metal or
plastic
–

materials that hold latent prints well


•
Can an attacker steal a token, lift the owner’s
latent prints from it, and construct a working
clone of the owner’s fingerprint?

•
Worse, can an attacker reactivate a latent
image of the biometric from the sensor itself?


•
Answer: in some cases, YES.

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Biometric Dilemma

Finger Cloning Effectiveness

•
Willis and Lee could trick 4 of 6 sensors tested
in 1998 with cloned fingers

•
Thalheim et al could trick both “capacitive” and
“optical” sensors with cloned fingers

–
Products from Siemens, Cherry, Eutron, Verdicom

–
Latent image reactivation only worked on capacitive sensors,
not on optical ones

•
Matsumoto tested 11 capacitive and optical
sensors

–
Cloned fingers tricked all of them

–
Compaq, Mitsubishi, NEC, Omron, Sony, Fujitsu, Siemens,
Secugen, Ethentica

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Biometric Dilemma

Summary

•
Traditional FAR and FRR statistics don’t tell the
whole story about biometric vulnerabilities

•
Networked biometrics require trusted readers
that pose extra administrative headaches

•
We can build physical clones of biometric
features that spoof biometric readers

–
Matsumoto needed $10 worth of materials and 40 minutes to
reliably clone a fingerprint

•
We can often build clones without the
legitimate user’s intentional participation

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Biometric Dilemma

Thank You!

Questions? Comments?


My e
-
mail:

Rick_Smith@securecomputing.com

http://www.visi.com/crypto


http://www.securecomputing.com