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Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability
Ozbulak, Utku; Peck, Jonathan; De Neve, Wesley et al.
202037th International Conference on Machine Learning; Workshop on Uncertainty & Robustness in Deep Learning (ICML UDL 2020)
Peer reviewed
 

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Disciplines :
Computer science
Mathematics
Author, co-author :
Ozbulak, Utku
Peck, Jonathan
De Neve, Wesley
Goossens, Bart
Saeys, Yvan
Van Messem, Arnout  ;  Université de Liège - ULiège > Département de mathématique > Statistique applquée aux sciences
Language :
English
Title :
Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability
Publication date :
2020
Event name :
37th International Conference on Machine Learning; Workshop on Uncertainty & Robustness in Deep Learning (ICML UDL 2020)
Event place :
Vienna, Austria
Event date :
2020
Audience :
International
Peer reviewed :
Peer reviewed
Source :
Available on ORBi :
since 10 March 2021

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