Biometric Authentication Revisited: Understanding the Impact of Wolves in Sheep Clothing

1
Biometric Authentication Revisited Understanding
the Impact of Wolves in Sheep Clothing

• Lucas Ballard, Fabian Monrose, Daniel Lopresti 
• USENIX Security Symposium, 2006
• Presenter Tao Li

2
Motivation

• To argue that previous assumption that forgers
  are minimally motivated and attacks can only be
  mounted by hand is too optimistic and even
  dangerous
• To show that the standard approach of evaluation
  significantly overestimates the security of the
  handwriting-based key-generation system

3
What did the authors do?

• In this paper, the author described their initial
  steps toward developing evaluation methodologies
  for behavior biometrics that take into account
  threat models which have largely been ignored.
• Presented a generative attack model based on
  concatenative synthesis that can provide a rapid
  indication of the security afforded by the system.

4
Outline

• Background Information
• Experimental Design
• Human Evaluation
• Generative Evaluation
• Conclusion

5
Background Information

• Obtaining human input as a system security
  measure
• Not reproducible by attackers
• Eg, passwords
• Online attackslimited to a number of wrong
  attemps
• Offline attackslimited only to the resources of
  the attackers, time memory.
• When use passwords to derive cryptographic keys,
  susceptible to offline attacks

6
What is biometric?

• An alternative form of user input intended
  difficultly to be reproduced by attackers
• A technique for user to authenticate himself to a
  reference monitor based on biometric
  characteristics
• A means for generating user-specific
  cryptographic keys. Can it survive offline
  attacks?Not sure
• Password hardening password biometric

7
Which is good biometric features?

• Traditional procedure of biometric as an
  authenticate paradigm
• Sampling an input from user
• Extracting an proper set of features
• Compare with previously stored templates
• Confirm or deny the claimed identity
• Good features exhibit
• Large inter-class variability
• Small intra-class variability

8
How to evaluate biometric systems?

• The standard model
• Enroll some users by collecting training samples,
  eg, handwriting or speech
• Test the rate at which users attempts to
  recreate the biometric within a predetermined
  tolerance fails--False Reject Rate (FRR).
• False Accept Rate (FAR) rate to fool the system
• Equal Error Rate (EER) where FRRFAR
• The lower EER, the higher the accuracy.

9
How to evaluate biometric systems?

• Commonly divided into naïve forgeries skilled
  forgeries
• Missing generative models to create synthetic
  forgeries
• Evaluation is misleading under such weak security
  assumptions which underestimates FAR.

10
Handwriting Biometrics

• As a first step to provide a strong methodology
  for evaluate performance, the authors developed a
  prototype toolkit using handwriting dynamics as a
  case in point.

11
Handwriting Biometrics

• Offline handwriting
• A 2-D bitmap, eg, a scan of a paper
• only spatial info.
• Features extracted from it like bounding boxes
  and aspect ratios, stroke densities in a
  particular region, curvature measurements.
• Online handwriting
• Sampling the position of a stylus tip over time
  on digitizing tablet or pen computer
• temporal and spatial info.
• Features includes all from offline and timing and
  stroke order information

12
Experimental Design

• Collect data over 2 months analyzing 6 different
  forgery styles
• Three standard evaluation metrics
• Naïvenot really forgeries, naturally forgeries
• Staticcreated after seeing static rendering of
  the target users passphrase
• Dynamicusing real-time rendering
• Three more realistic metrics
• Naïve--similar to naïve, except similar writing
  style attacker
• Trainedforgeries after attackers are trained
• Generativeexploit info to algorithmically
  generate forgery

13
Data Collection

• 11,038 handwriting samples collected on digitized
  pen tablet computers from 50 users during 3
  rounds

14
Data Collection

• Round one 1 hour, two data sets
• First set established a baseline of typical
  user writing
• 5 different phrases2 words oxymoron, ten times
  each
• Establish biometric templates for authentication
• Samples for naïve and naïve forgeries
• Second data set, the generative corpus
• To create the generative forgeries
• Consists of a set of 65 oxymoron

15
Data Collection

• Round 2, 90 min, 2 weeks later
• Same users wrote the 5 phases of round 1 ten
  times, forge representative samples of round 1 to
  create 2 sets of 17 forgeries
• Static forgeriesseeing only static
  representation
• Dynamic forgeriesseeing a real-time rendering of
  the phrase

16
Data Collection

• Round 3, select nine users and train them
• Exhibit a natural tendency of better forgery
• 3 skilled but untrained users each writing style
  cursive, mixed, block
• Train them forge 15 samples from their own
  writing styles with real-time reproduction of the
  target sample.

17
Authentication System

• Users writing sample on the electronic tablet
  represented by 3 signals over time
• x(t), y(t) for location of the pen
• p(t) for pen up or down at time t
• Tablet computes a set of n statistical features
  (f1,f2,..fn) over the signals

18
Authentication System

• Based on the variation of feature values in a
  passphrase written m times and human natural
  variations, generate a n2 matrix template
  l1,h1,..ln,hn.
• Compare the user sample with feature values
  f1,f2,,fn with it. Each fjltlj or fjgthj results
  in an error.

19
Feature analysis

• Not only the entropy of each feature, but rather
  how difficult the feature is to forge
• For each feature f
• Rf proportion of times that f was missed by
  legitimate users
• Af proportion of times that f was missed by
  forgers from round 2
• Q(f)(Af-Rf1)/2
• Q(f) more closer to 1, the feature more desirable

20
Feature analysis

• Divide feature set into temporal and spatial
  groups and order them based on Q(f), chose top 40
  from each group and discard any with a FRR
  greater than 10, finally got 15 spatial and 21
  temporal features.

21
Human Evaluation
22
Human Evaluation
23
Human Evaluation

• At seven errors, the trained mixed, block and
  cursive forgers improved their FAR by 0.47, 0.34
  and 0.18.
• This improvements results from less than 2 hours
  training

24
Generative Evaluation

• Fining and training skilled forgers is time
  consuming
• To explore the use of an automated approach using
  generative models as a supplementary techniques
  for evaluating behavioral biometrics.
• To investigate whether an automated approach,
  using a limited writing samples from the target,
  could match the false accept rates observed for
  the trained forgers

25
Generative Evaluation

• The approach to synthesize handwriting is to
  assemble a collection of basic units (n-grams)
  that can be combined in a concatenative fashion
  to mimic authentic handwriting.
• The basic units are obtained from
• General population statistics
• Statistics specific to a demographic of the
  targeted user
• Data gathered from the targeted user

26
Generative Evaluation
27
Generative Evaluation

• Generative signature using some basic units from
  the database as above
• Original signature shown below

28
Generative Evaluation

• Limit 15 out of the 65 samples of target user and
  15 samples of same style users
• Result generative attempt only used 6.67 target
  users writing samples and the average length of
  an n-gram was 1.64 characters

29
Conclusion

• The authors argued in detail that current
  evaluation of security of biometric system is not
  accurate, underestimating the threat
• To prove this, they analyzed a handwriting-based
  key-generation system and show that the standard
  approach of evaluation significantly
  overestimates its security

30
Conclusion

• Present a generative attack model based on
  concatenative synthesis that automatically
  produce generative forgeries
• The generative approach matches or exceeds the
  effectiveness of forgeries rendered by trained
  humans

31
Weakness Where to improve

• The handwriting-based key-generation system needs
  lots of people and work.
• It remains unclear as to the extent to which
  these forgeries would fool human judges,
  especially forensic examiners
• The generative algorithm needs improvement like
  incorporating other parameters in it to make it
  more accurate.

32

• Thanks!
• Any Questions?