Hitoshi Kokumai

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Serious Funny Reality

FRR (False Rejection Rages)

 

False Acceptance Rates and False Rejection Rates

 

10

10%

10°

    
    

38 (Equa ror Rates)

procucts
(more accurate)

  

 
 

00 100 ar 100 10
FAR (Fale Accegtarce Rates)

I take up this new report today - “Researchers Defeated Advanced Facial Recognition Tech Using Makeup” https://www.vice.com/en/article/k78v9m/researchers-defeated-advanced-facial-recognition-tech-using-makeup

The report says “The experiment saw 100 percent success in the digital experiments on both the FaceNet model and the LResNet model, according to the paper. In the physical experiments, the participants were detected in 47.6 percent of the frames if they weren’t wearing any makeup and 33.7 percent of the frames if they wore randomly applied makeup. Using the researchers’ method of applying makeup to the highly identifiable parts of the attacker’s face, they were only recognized in 1.2 percent of the frames.”

An empirical match rate of 1.2%, when ‘made-up’, is astonishing. Nearly as amazing is the match rate of 47.6% when there is no such ‘make-up’. 

These figures could enable us to start a realistic discussion about the security versus privacy issue of biometrics – is it a threat to security or a threat to privacy or a threat to both of them?

Incidentally, a researcher reportedly mentioned “I think the technology is becoming better and better”, which might or might not be followed by such a remark as “Therefore, this crude immature technology must be deployed right now for the very serious identity businesses”.

------------------------

Our view on the probabilistic biometrics is summarized here – https://www.linkedin.com/pulse/what-2-graphs-tell-us-biometrics-hitoshi-kokumai">“What these 2 graphs tell us about biometrics”  

and “Biometrics is to Password what Back door is to Front door”  

PS Such an eye-catching makeup shown above is avoided in the research.

FRR (False Rejection Rages)<br /> <br />  <br /> <br /> False Acceptance Rates and False Rejection Rates<br /> <br />  <br /> <br /> 10<br /> <br /> 10%<br /> <br /> 10°<br /> <br />     <br />     <br /> <br /> 38 (Equa ror Rates)<br /> <br /> procucts<br /> (more accurate)<br /> <br />   <br /> <br />  <br />  <br /> <br /> 00 100 ar 100 10<br /> FAR (Fale Accegtarce Rates)Worry about a backdoor?

Key References 

Biometrics is to Password what Back door is to Front door


Removal of Passwords and Its Security Effect 

Negative Security Effect of Biometrics Deployed in Cyberspace

External Body Features Viewed as ‘What We Are’

Secret Credenti

 
   
 

Memories

Episodic Memory


Additional References
 

For Achieving Solid Digital Identity on Information Security Buzz (Mar/2021)

What We Know for Certain about Authentication Factors

Digital Identity for Global Citizens
 Image-to-Code Conversion by Expanded Password System

Summary and Brief History - Expanded Password System

Proposition on How to Build Sustainable Digital Identity Platform

Account Recovery with Expanded Password System 

 History, Current Status and Future Scenarios of Expanded Password System 

Availability-First Approach 

Update: Questions and Answers - Expanded Password System and Related Issues 

DED

RDaDHP
BOP v6
mec Ne

gr |ar

Generating High-
Entropy Password like

xtyax9d4294dlelEYVz
wo/gadieowUx093/x7
?lwble84x09xloPxLxeo
dtyYDidex&&xeigo@y...

 < Videos on YouTube>
 

Slide: Outline of Expanded Password System (3minutes 2seconds)

Digital Identity for Global Citizens (10minutes - narrated)

Demo: Simplified Operation on Smartphone for consumers (1m41s)

Demo: High-Security Operation on PC for managers (4m28s)

Demo: Simple capture and registration of pictures by users (1m26s)

Slide: Biometrics in Cyber Space - "below-one" factor authentication

CV9X2.png
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