SBIR-STTR Award

Real-time Enhanced Voice Authentication
Award last edited on: 5/20/2023

Sponsored Program
SBIR
Awarding Agency
DOD : DARPA
Total Award Amount
$3,398,656
Award Phase
2
Solicitation Topic Code
SB163-006
Principal Investigator
Ewald Enzinger

Company Information

Eduworks Corporation (AKA: Eduworks)

400 SW 4th Street Suite 110
Corvallis, OR 97333
   (541) 753-0844
   info@eduworks.com
   www.eduworks.com
Location: Single
Congr. District: 04
County: Benton

Phase I

Contract Number: D17PC00166
Start Date: 3/21/2017    Completed: 4/10/2018
Phase I year
2017
Phase I Amount
$149,849
Voice phishing (vishing) has become a serious threat. Attackers pose as trusted callers using impersonation, voice mimicry, speech synthesis, voice conversion technologies, and many other techniques. Once victims believes they are speaking with a t...

Phase II

Contract Number: 140D6318C0064
Start Date: 6/7/2018    Completed: 4/14/2022
Phase II year
2018
(last award dollars: 2021)
Phase II Amount
$3,248,807

Voice phishing ("vishing") is an increasingly serious threat that cost victims over $46 billion in 2016. Attackers pose as trusted callers using impersonation, voice mimicry, speech synthesis, voice conversion, Caller ID spoofing, and other techniques. Once victims trust the caller, the perpetrator can engage in fraudulent and criminal activities leading to financial losses and security breaches. The proposed research will result in a Real-time Enhanced Voice Authentication (REVA) system that detects many common forms of vishing, is available as a SaaS solution and mobile app, and serves to verify known callers in real-time. REVA builds on and extends techniques used in automated interactive voice response systems and audio forensics to detect signal alterations and to verify known speakers. REVA will monitor voice streams in real time and use a machine learning algorithm to continually estimate the likelihood that vishing is taking place, alerting users to dangers before they reveal sensitive information. This capability will be of great value to the security and defense communities, call centers, businesses, and consumers. The proposed architecture and machine learning approach give REVA the ability to learn to detect new types of attacks and to be updated as network characteristics and communication technologies evolve.