DagRep.12.7.41.pdf
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Security of Machine Learning (Dagstuhl Seminar 22281)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 12, Issue 7
Volume: Dagstuhl Reports, Volume 12
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-02-03
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Battista Biggio, Nicholas Carlini, Pavel Laskov, Konrad Rieck, and Antonio Emanuele Cinà. Security of Machine Learning (Dagstuhl Seminar 22281). In Dagstuhl Reports, Volume 12, Issue 7, pp. 41-61, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/DagRep.12.7.41
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.
Subject Classification
ACM Subject Classification
- Computer systems organization → Real-time operating systems
- Computing methodologies → Machine learning
Keywords
- adversarial machine learning
- machine learning security
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