Voice Biometric Overview for SfTelephony Meetup

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Voice Biometric Overview for SfTelephony Meetup. March 10, 2011 Dan Miller Opus Research. Why I’m here. Talk about voice biometrics Share some ideas on stronger authentication for mobile transactions Get your feedback as prospective users/developers/implementers - PowerPoint PPT Presentation

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Voice Biometric Overview for

SfTelephony MeetupMarch 10, 2011

Dan MillerOpus Research

© 2011 Opus Research, Inc.

Why I’m here Talk about voice biometrics Share some ideas on stronger

authentication for mobile transactions Get your feedback as prospective

users/developers/implementers Describe some “real world” use cases,

business cases and demand drivers

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© 2011 Opus Research, Inc. Page 4

Voice Biometrics and Speaker Verification

Voice Biometrics is a technology Captures an utterance from a live caller Compares it to previously stored “voiceprint” Produces a score

Speaker Verification is an application Employs a biometric engine plus business logic Enrolls customers by obtaining voice prints Compares live utterances to voice prints to

produce a “pass” or “fail” responses

© 2011 Opus Research, Inc. Page 4

Speaker Verification Components Core Verification Engine

Receives voice sample (“utterance”); compares it to a voiceprint (“template”)

Confirms who said it Core Recognition Engine

Compares utterance to ASR grammar Determines what was said

Business Logic Decides if the caller passes or fails Dictates required “next steps”

© 2011 Opus Research, Inc. Page 5

What is a Voice Print?Physical Characteristics The unique physical traits of the individual’s vocal tract, such as shape and size.

Behavioral Characteristics The harmonic and resonant frequencies, such as accents, the speed of your speech, and how words are pronounced and emphasized.

Voiceprint - Together these physiological and behavioral factors combine to produce unique voice patterns for every individual

© 2011 Opus Research, Inc. Page 6

Verification vs. Identification

For Verification: User claims an ID Application matches voiceprint to that claim

For Identification: No claim of identity ID System tries to detect “closest match” of

captured utterances to voiceprint from a population of registered users

© 2011 Opus Research, Inc. Page 7

Text Dependent vs. Text Independent

Applications that require a specific pass phrase are Text Dependent Require training Customarily involve enrollment

Text Independent applications can use any utterance Simplify enrollment Support “conversational authentication”

© 2011 Opus Research, Inc.

Why Now?

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© 2011 Opus Research, Inc.

Fraud protection persistence

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Multifactor Mandated in more use cases Includes “something you are”

Multimodal Because “the customer is always on” Embraces social networks and multiple sign-

ons Mobile

Approaching 6 billion subscribers Mobile devices are becoming virtual

assistants

© 2011 Opus Research, Inc.

+1 = Momentum Passwords getting more difficult

Multiple digits and special characters Frequently updated Fragmented across sites (and IDs)

Authentication becoming important To access multiple sites, domains and devices For more activities, transactions and

interactions “Open” approaches only as strong as weakest

link10

© 2011 Opus Research, Inc.

Application strengths Mobile payment authorization Device activation Access control Password reset Anonymous authentication

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© 2011 Opus Research, Inc.

Perspectives from RSA

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The “Phone Channel” Traditionally Has Weaker Security

ANI detectionVoice profile (gender, age etc.) based on intuitionPhone numberAddressWeak Identity verification • Mother’s maiden name• Social Security Number• Basic account knowledge (last purchase etc.)

Fraudster call center online

order form(with English translation)

• “Professional callers”: fluent in numerous languages, both male and female

• Caller-ID spoofing

• Service availability during American and Western European business hours.

• Cost: $7-$15 per phone call,

• Complete fraudulent transactions by impersonating people across a broad spectrum of demographics • i.e. 77-year old female fluent

in English or a middle-aged man fluent in Italian.

Fraudster-Operated Call Centers Emerge in the Underground Economy to Facilitate Phone Fraud

Fraudster Operated Call Centers

Underground forum post advertising "Professional Call Service"

Fraudster Operated Call Centers

Review of a fraudster call center service

* Available H1 2008

How Multi-Channel Fraud is Perpetrated

Tools of the trade:• VOIP (IPBX)

• ID Spoofing

Delivery:• War dialing

• SMS

• Email

Already in play in the US

Vishing

How Fraudsters Bypass Blacklisted Call Center Numbers

Fraudster calls Spoofing access

point

Directs call to non-blacklisted phone number with Spoofed Caller ID

Call Forwarding Device

Call is forwarded to call center 800 number

Call Center services unsuspicious inbound call displaying spoofed ID of an

existing customer

Fraudsters’ Interest in Phone Banking

© 2011 Opus Research, Inc.

And Speaker V & I can help Questions?

Contact: dmiller@opusresearch.netOr on Twitter @dnm54

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