<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  
    
  <dc:date>2020-10</dc:date>
    
  
    
  <dc:source>ERCIM News(123), 36-37 (2020-10)</dc:source>
    
  
    
  <dc:description xml:lang="eng">Securing complex systems is an important challenge,
especially in critical systems. Artificial intelligence (AI),
which is increasingly used in critical domains such as
medical diagnosis, requires special treatment owing to
the difficulties associated with explaining AI decisions.
Currently, to perform an intensive security evaluation of
systems that rely on AI, testers need to resort to blackbox
(penetration) testing.</dc:description>
    
  
    
  <dc:rights>All rights reserved</dc:rights>
    
  
    
  <dc:format>application/pdf</dc:format>
    
  
    
  <dc:title xml:lang="deu">Penetration Testing Artificial Intelligence</dc:title>
    
  
    
  <dc:language>eng</dc:language>
    
  
    
  <dc:creator>Tjoa, Simon (St. Pölten UAS, Austria)</dc:creator>
    
  <dc:creator>Buttinger, Christina (Austrian Armed Forces)</dc:creator>
    
  <dc:creator>Holzinger, Katharina (Austrian Armed Forces)</dc:creator>
    
  <dc:creator>Kieseberg, Peter (St. Pölten UAS, Austria)</dc:creator>
    
  
    
  <dc:type xml:lang="eng">article</dc:type>
    
  
    
  <dc:identifier>https://phaidra.ustp.at/o:4338</dc:identifier>
    
  
</oai_dc:dc>