2023
DOI: 10.1007/978-3-031-37742-6_27
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Attacking and Defending Printer Source Attribution Classifiers in the Physical Domain

Abstract: The security of machine learning classifiers has received increasing attention in the last years. In forensic applications, guaranteeing the security of the tools investigators rely on is crucial, since the gathered evidence may be used to decide about the innocence or the guilt of a suspect. Several adversarial attacks were proposed to assess such security, with a few works focusing on transferring such attacks from the digital to the physical domain. In this work, we focus on physical domain attacks against … Show more

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