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dc.contributor.authorAmirian, Mohammadreza-
dc.contributor.authorSchwenker, Friedhelm-
dc.contributor.authorStadelmann, Thilo-
dc.date.accessioned2018-07-13T07:08:44Z-
dc.date.available2018-07-13T07:08:44Z-
dc.date.issued2018-
dc.identifier.isbn978-3-319-99977-7de_CH
dc.identifier.isbn978-3-319-99978-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/8027-
dc.description.abstractThe existence of adversarial attacks on convolutional neural networks (CNN) questions the fitness of such models for serious applications. The attacks manipulate an input image such that misclassification is evoked while still looking normal to a human observer – they are thus not easily detectable. In a different context, backpropagated activations of CNN hidden layers – “feature responses” to a given input – have been helpful to visualize for a human “debugger” what the CNN “looks at” while computing its output. In this work, we propose a novel detection method for adversarial examples to prevent attacks. We do so by tracking adversarial perturbations in feature responses, allowing for automatic detection using average local spatial entropy. The method does not alter the original network architecture and is fully human-interpretable. Experiments confirm the validity of our approach for state-of-the-art attacks on large-scale models trained on ImageNet.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes in Computer Sciencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectModel interpretabilityde_CH
dc.subjectFeature visualizationde_CH
dc.subjectDiagnosticde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleTrace and detect adversarial attacks on CNNs using feature response mapsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1007/978-3-319-99978-4_27de_CH
dc.identifier.doi10.21256/zhaw-3863-
zhaw.conference.details8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end358de_CH
zhaw.pages.start346de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.series.number11081de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsArtificial Neural Networks in Pattern Recognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.funding.zhawQualitAI - Quality control of industrial products via deep learning on imagesde_CH
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Amirian, M., Schwenker, F., & Stadelmann, T. (2018). Trace and detect adversarial attacks on CNNs using feature response maps [Conference paper]. Artificial Neural Networks in Pattern Recognition, 346–358. https://doi.org/10.1007/978-3-319-99978-4_27
Amirian, M., Schwenker, F. and Stadelmann, T. (2018) ‘Trace and detect adversarial attacks on CNNs using feature response maps’, in Artificial Neural Networks in Pattern Recognition. Springer, pp. 346–358. Available at: https://doi.org/10.1007/978-3-319-99978-4_27.
M. Amirian, F. Schwenker, and T. Stadelmann, “Trace and detect adversarial attacks on CNNs using feature response maps,” in Artificial Neural Networks in Pattern Recognition, 2018, pp. 346–358. doi: 10.1007/978-3-319-99978-4_27.
AMIRIAN, Mohammadreza, Friedhelm SCHWENKER und Thilo STADELMANN, 2018. Trace and detect adversarial attacks on CNNs using feature response maps. In: Artificial Neural Networks in Pattern Recognition. Conference paper. Springer. 2018. S. 346–358. ISBN 978-3-319-99977-7
Amirian, Mohammadreza, Friedhelm Schwenker, and Thilo Stadelmann. 2018. “Trace and Detect Adversarial Attacks on CNNs Using Feature Response Maps.” Conference paper. In Artificial Neural Networks in Pattern Recognition, 346–58. Springer. https://doi.org/10.1007/978-3-319-99978-4_27.
Amirian, Mohammadreza, et al. “Trace and Detect Adversarial Attacks on CNNs Using Feature Response Maps.” Artificial Neural Networks in Pattern Recognition, Springer, 2018, pp. 346–58, https://doi.org/10.1007/978-3-319-99978-4_27.


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