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Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection

  • Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to the human eye. In recent years, various approaches have been proposed to defend CNNs against such attacks,Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to the human eye. In recent years, various approaches have been proposed to defend CNNs against such attacks, for example by model hardening or by adding explicit defence mechanisms. Thereby, a small “detector” is included in the network and trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. In this work, we propose a simple and light-weight detector, which leverages recent findings on the relation between networks’ local intrinsic dimensionality (LID) and adversarial attacks. Based on a re-interpretation of the LID measure and several simple adaptations, we surpass the state-of-the-art on adversarial detection by a significant m argin and reach almost perfect results in terms of F1-score for several networks and datasets. Sources available at: https://github.com/adverML/multiLIDshow moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/8237
Bibliografische Angaben
Title (English):Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection
Conference:International Conference on Computer Vision Theory and Applications (18. : 19-21 February, 2023 : Lisbon, Portugal)
Author:Peter Lorenz, Margret Keuper, Janis KeuperStaff MemberORCiDGND
Year of Publication:2023
Publisher:SciTePress
Page Number:27
First Page:38
Parent Title (English):Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume:5
ISBN:978-989-758-634-7
ISSN:2184-4321
DOI:https://doi.org/10.5220/0011586500003417
URL:https://www.scitepress.org/PublishedPapers/2023/115865/115865.pdf
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Collections of the Offenburg University:Bibliografie
Tag:Deep Leaning
Formale Angaben
Relevance for "Jahresbericht über Forschungsleistungen":Konferenzbeitrag: h5-Index < 30
Open Access: Open Access 
 Diamond 
Licence (German):License LogoCreative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International