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Research Outline
Prepared for Amanda K. | Delivered December 13, 2019
Counter AI Technologies
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Goals
To find information on the current research that exists in regard to counter AI such as deep fakes and biometric spoofing.
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Early Findings
Biometric authentication is one of the most used security technology in
the digital ID space
.
An instance of this trend is in the smartphone sector where brands like Apple have developed
biometric unlock features
using fingerprints and facial features.
However, "
fraudsters, hackers
and other cybercriminals are unfortunately working to circumvent these defenses and
access valuable user data
, but biometrics providers are creating measures to anticipate and counter the latest threats."
Different methods are used by cyber criminals to
beat biometric authentication
measures. A major method is
spoofing
, which is the security industry term for
faking biometric identifiers
to impersonate legitimate users and gain access.
Technologies such as
artificial intelligence, machine learning
, 3D printing, and advanced sensors are double-edged swords because they can be used to develop tools that improve security and they can also be used to develop tools that utilize these technologies
to break defenses.
Spoofing relies on creating
fake physical characteristics
such as facial features, fingerprints or vein patterns
s
o
a
s
t
o
trick sensors into recognizing users who are not actually there.
In 2018,
German security researchers
at the Chaos Communication Congress conference demonstrated a fake hand designed to outsmart palm-vein scanners (the hand was designed in about one month). This shows that counter AI technologies can be used
to break security
by tricking AI based security systems.
Additional, facial recognition biometrics can
also be spoofed
with certain technologies and researchers from the University of North Carolina "
used rendering software
to create 3D models of human heads, based on publicly accessible Facebook pictures."
The researchers then used a VR system to submit the models to facial recognition programs that included
BioID, KeyLemon,
M
o
b
i
u
s
, True Key, and 1D. They ended up successfully spoofing
four of the facial recognition
programs with success rates that ranged between
55% and 85%
.
There exists numerous peer reviewed research papers on the topic
of deep fakes
and
biometric spoofing
.
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