Biometric Authentication Revisited:

Understanding the Impact of Wolves

in Sheep’s clothingin Sheep’s clothingLucas Ballard, Fabian Monrose, Daniel Lopresti

Presented by : Anuj Sawani

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Biometrics

• What is it?

– identifying, or verifying a person based on

• Physiological characteristics

• Behavioral characteristics

– Examples?– Examples?

• Biometric Authentication vs Identification

– “Am I who I claim to be?”

– “Who am I?”

• Better than passwords?

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Handwriting as a biometric

• Offline

– 2-D bitmap

• Online

– Real-time data– Real-time data

• Signatures as a biometric?

Feature extraction Hash/Key

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So, what’s with the menagerie?

• Sheep

– Easily accepted by the system

• Goats

– Exceptionally unsuccessful at being accepted– Exceptionally unsuccessful at being accepted

• Lambs

– Exceptionally vulnerable to imitations

• Wolves

– Exceptionally successful at imitations

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The Threat Model

• Exploiting poorly protected template

databases

• Eavesdropping communication between • Eavesdropping communication between

sensor and the system

• Presenting artificially created samples to the

sensor

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A neat idea – Concatenation attack

• Samples of user’s handwriting from other

contexts

• General samples of the style of writing

• Feature analysis …• Feature analysis …

• Generate the user’s handwriting synthetically!

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Performance Statistics

False Accept Rate (FAR) False Reject Rate (FRR)

Equal Error Rate (ERR)

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Forgery styles

• Naïve

– Use other users’ writing as it was naturally rendered to forge the passphrase

• Naïve*• Naïve*

– Similar to Naïve, but uses similar writing styles

• Static

– Forgery using an image of the passphrase

• Dynamic

– Real-time rendering of the passphrase

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Grooming the sheep into wolves

• 11,038 handwriting samples

• Incentives awarded to consistent writers,

“dedicated forgers”

• Three Rounds• Three Rounds

1. Collect the samples

2. Static and Dynamic forging

3. Selected “trained” forgers

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Handwriting features

• How difficult is the feature to forge?

• Signals – t, x(t), y(t), p(t)

• For every feature f

– rf � missed by legitimate users– rf � missed by legitimate users

– af � missed by forgers

• Quality metric

– Q = (af - rf + 1)/2

• Q = 0 – never reliably reproduced by users

• Q = 1 – never reproduced by forgers

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The winning features

• The probability that the ith stroke of c1

connects c2

• Median gap between the adjacent characters

• Median time between end of c and beginning • Median time between end of c1 and beginning

of c2

• Pen-up velocity

• A total of 36 good features out of 144

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Algorithm to generate a known

passphrase• Select n-grams from different context such that

– g1 || g2 || … ||gk = passphrase

• Normalize t, x(t) and y(t) – match baselines

• Spatial adjustment of x(t)– Use median gap feature

• Fabricate p(t)• Fabricate p(t)– Use probability of connection feature

– Delayed strokes pushed into stack• Executed after each pen-up

• Add time delays– Use median time feature

– Use pen-up velocity and distance between strokes

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The system at work…

• Used small sample set of 15 samples of user’s writing

– Each character from passphrase exists in set

– Does not include passphrase– Does not include passphrase

• Also, used 15 samples of similar writing style

• The algorithm caused an EER of 27.4%

– Forgers caused an EER of 20.6%

• n-gram length < 2

• Used 6.67 of the samples on average

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Conclusion

• Handwriting as a reliable biometric?

– Refutable

• Adversary has been under-estimated till now

• Generative approach produces better • Generative approach produces better

forgeries than trained humans

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Take away

Watch out for the next generation

of wolves!

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