From 4d7efaead4641d84f15cb38b00ebc6953878f259 Mon Sep 17 00:00:00 2001 From: adamhrv Date: Sun, 7 Jul 2019 13:21:27 +0200 Subject: update msc --- .../assets/7208430726.jpg | Bin 77202 -> 76194 bytes .../research/munich_security_conference/index.md | 4 ++-- .../research/munich_security_conference/index.html | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/site/content/pages/research/munich_security_conference/assets/7208430726.jpg b/site/content/pages/research/munich_security_conference/assets/7208430726.jpg index 29cfcadb..1404c6f6 100755 Binary files a/site/content/pages/research/munich_security_conference/assets/7208430726.jpg and b/site/content/pages/research/munich_security_conference/assets/7208430726.jpg differ diff --git a/site/content/pages/research/munich_security_conference/index.md b/site/content/pages/research/munich_security_conference/index.md index 7fada8c7..29b278a9 100644 --- a/site/content/pages/research/munich_security_conference/index.md +++ b/site/content/pages/research/munich_security_conference/index.md @@ -21,8 +21,8 @@ authors: Adam Harvey + Images Analyzed: 24,302,637 + Datasets Analyzed: 30 + Years: 2006 - 2018 -+ Status: Ongoing Investigation -+ Last Updated: June 28, 2019 ++ Last Updated: July 7, 2019 ++ Text and Research: Adam Harvey ### end sidebar diff --git a/site/public/research/munich_security_conference/index.html b/site/public/research/munich_security_conference/index.html index 69092928..fc44bfd8 100644 --- a/site/public/research/munich_security_conference/index.html +++ b/site/public/research/munich_security_conference/index.html @@ -57,7 +57,7 @@
Transnational Flows of Face Recognition Image Training Data
Where does face data originate and who's using it?

A case study on publicly available facial recognition datasets for the Munich Security Conference's Transnational Security Report

-
Images Analyzed
24,302,637
Datasets Analyzed
30
Years
2006 - 2018
Status
Ongoing Investigation
Last Updated
June 28, 2019

National AI strategies often rely on transnational data sources to capitalize on recent advancements in deep learning and neural networks. Researchers benefiting from these transnational data flows can yield quick and significant gains across diverse sectors from health care to biometrics. But new challenges emerge when national AI strategies collide with national interests.

+
Images Analyzed
24,302,637
Datasets Analyzed
30
Years
2006 - 2018
Last Updated
July 7, 2019
Text and Research
Adam Harvey

National AI strategies often rely on transnational data sources to capitalize on recent advancements in deep learning and neural networks. Researchers benefiting from these transnational data flows can yield quick and significant gains across diverse sectors from health care to biometrics. But new challenges emerge when national AI strategies collide with national interests.

Our earlier research on the MS Celeb and Duke datasets published with the Financial Times revealed that several computer vision image datasets created by US companies and universities were unexpectedly also used for research by the National University of Defense Technology in China, along with top Chinese surveillance firms including SenseTime, SenseNets, CloudWalk, Hikvision, and Megvii/Face++ which have all been linked to oppressive surveillance in the Xinjiang region of China.

In this new research for the Munich Security Conference's Transnational Security Report we provide summary statistics about the origins and endpoints of facial recognition information supply chains. To make it more personal, we gathered additional data on the number of public photos from embassies that are currently being used in facial recognition training datasets.

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