From 27340ac4cd43f8eec7414495b541a65566ae2656 Mon Sep 17 00:00:00 2001
From: adamhrv <adam@ahprojects.com>
Date: Tue, 8 Oct 2019 16:02:47 +0200
Subject: update site, white

---
 site/public/research/index.html                            | 2 +-
 site/public/research/munich_security_conference/index.html | 5 ++---
 2 files changed, 3 insertions(+), 4 deletions(-)

(limited to 'site/public/research')

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       <a href='/research/munich_security_conference/'><section class='wide' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/site/research/munich_security_conference/assets/background.jpg);' />
         <section>
           <h4><span class='bgpad'>28 June 2019</span></h4>
-          <h2><span class='bgpad'>Analyzing Transnational Flows of Face Recognition Image Training Data</span></h2>
+          <h2><span class='bgpad'>Transnational Flows of Face Recognition Image Training Data</span></h2>
           <h3><span class='bgpad'>Where does face data originate and who's using it?</span></h3>
           <h4 class='readmore'><span class='bgpad'>Read more...</span></h4>
         </section>
diff --git a/site/public/research/munich_security_conference/index.html b/site/public/research/munich_security_conference/index.html
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   </header>
   <div class="content content-dataset">
     
-  <section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/site/research/munich_security_conference/assets/background.jpg)'><div class='inner'><div class='hero_desc'><span class='bgpad'>Transnational Flows of Face Recognition Image Training Data</span></div><div class='hero_subdesc'><span class='bgpad'>Where does face data originate and who's using it?
-</span></div></div></section><section><p><em>A case study on publicly available facial recognition datasets for the Munich Security Conference's Transnational Security Report</em></p>
-</section><section><div class='right-sidebar'><div class='meta'><div class='gray'>Images Analyzed</div><div>24,302,637</div></div><div class='meta'><div class='gray'>Datasets Analyzed</div><div>30</div></div><div class='meta'><div class='gray'>Years</div><div>2006 - 2018</div></div><div class='meta'><div class='gray'>Last Updated</div><div>July 7, 2019</div></div><div class='meta'><div class='gray'>Text and Research</div><div>Adam Harvey</div></div></div><p>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.</p>
+  <section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/site/research/munich_security_conference/assets/background.jpg)'></section><section><p><em>A case study on publicly available facial recognition datasets for the Munich Security Conference's Transnational Security Report</em></p>
+</section><section><div class='right-sidebar'><div class='meta'><div class='gray'>Images Analyzed</div><div>24,302,637</div></div><div class='meta'><div class='gray'>Datasets Analyzed</div><div>30</div></div><div class='meta'><div class='gray'>Years</div><div>2006 - 2018</div></div><div class='meta'><div class='gray'>Last Updated</div><div>July 7, 2019</div></div><div class='meta'><div class='gray'>Text and Research</div><div>Adam Harvey</div></div><div class='meta'><div class='gray'>Published in</div><div><a href="https://tsr.securityconference.de/">Transnational Security Report</a></div></div></div><p>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.</p>
 <p>Our <a href="https://www.ft.com/content/cf19b956-60a2-11e9-b285-3acd5d43599e">earlier research</a> on the <a href="/datasets/msceleb">MS Celeb</a> and <a href="/datasets/duke_mtmc">Duke</a> 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.</p>
 <p>In this new research for the <a href="https://tsr.securityconference.de">Munich Security Conference's Transnational Security Report</a> 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.</p>
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