License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.12.7.41
URN: urn:nbn:de:0030-drops-176117
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17611/
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Biggio, Battista ; Carlini, Nicholas ; Laskov, Pavel ; Rieck, Konrad ; Cinà, Antonio Emanuele
Weitere Beteiligte (Hrsg. etc.): Battista Biggio and Nicholas Carlini and Pavel Laskov and Konrad Rieck and Antonio Emanuele Cinà

Security of Machine Learning (Dagstuhl Seminar 22281)

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dagrep_v012_i007_p041_22281.pdf (2 MB)

Abstract

Machine learning techniques, especially deep neural networks inspired by mathematical models of human intelligence, have reached an unprecedented success on a variety of data analysis tasks. The reliance of critical modern technologies on machine learning, however, raises concerns on their security, especially since powerful attacks against mainstream learning algorithms have been demonstrated since the early 2010s. Despite a substantial body of related research, no comprehensive theory and design methodology is currently known for the security of machine learning. The proposed seminar aims at identifying potential research directions that could lead to building the scientific foundation for the security of machine learning. By bringing together researchers from machine learning and information security communities, the seminar is expected to generate new ideas for security assessment and design in the field of machine learning.

BibTeX - Entry

@Article{biggio_et_al:DagRep.12.7.41,
  author =	{Biggio, Battista and Carlini, Nicholas and Laskov, Pavel and Rieck, Konrad and Cin\`{a}, Antonio Emanuele},
  title =	{{Security of Machine Learning (Dagstuhl Seminar 22281)}},
  pages =	{41--61},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{7},
  editor =	{Biggio, Battista and Carlini, Nicholas and Laskov, Pavel and Rieck, Konrad and Cin\`{a}, Antonio Emanuele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/17611},
  URN =		{urn:nbn:de:0030-drops-176117},
  doi =		{10.4230/DagRep.12.7.41},
  annote =	{Keywords: adversarial machine learning, machine learning security}
}

Keywords: adversarial machine learning, machine learning security
Collection: DagRep, Volume 12, Issue 7
Issue Date: 2023
Date of publication: 03.02.2023

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