Adversarial sample crafting for time series classification with elastic similarity measures

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dc.contributor.authorOregi, Izaskun
dc.contributor.authorDel Ser, Javier
dc.contributor.authorPerez, Aritz
dc.contributor.authorLozano, Jose A.
dc.contributor.institutionQuantum
dc.date.accessioned2024-07-24T11:47:46Z
dc.date.available2024-07-24T11:47:46Z
dc.date.issued2018
dc.descriptionPublisher Copyright: © 2018, Springer Nature Switzerland AG.
dc.description.abstractAdversarial Machine Learning (AML) refers to the study of the robustness of classification models when processing data samples that have been intelligently manipulated to confuse them. Procedures aimed at furnishing such confusing samples exploit concrete vulnerabilities of the learning algorithm of the model at hand, by which perturbations can make a given data instance to be misclassified. In this context, the literature has so far gravitated on different AML strategies to modify data instances for diverse learning algorithms, in most cases for image classification. This work builds upon this background literature to address AML for distance based time series classifiers (e.g., nearest neighbors), in which attacks (i.e. modifications of the samples to be classified by the model) must be intelligently devised by taking into account the measure of similarity used to compare time series. In particular, we propose different attack strategies relying on guided perturbations of the input time series based on gradient information provided by a smoothed version of the distance based model to be attacked. Furthermore, we formulate the AML sample crafting process as an optimization problem driven by the Pareto trade-off between (1) a measure of distortion of the input sample with respect to its original version; and (2) the probability of the crafted sample to confuse the model. In this case, this formulated problem is efficiently tackled by using multi-objective heuristic solvers. Several experiments are discussed so as to assess whether the crafted adversarial time series succeed when confusing the distance based model under target.en
dc.description.sponsorshipThis work has been supported by the Basque Government through the EMAITEK, BERC 2014–2017 and the ELKARTEK programs, and by the Spanish Ministry of Economy and CompetitivenessMINECO: BCAMSevero Ochoa excellence accreditation SVP-2014-068574 and SEV-2013-0323, and through the project TIN2017-82626-R funded by (AEI/FEDER, UE). Acknowledgments. This work has been supported by the Basque Government through the EMAITEK, BERC 2014–2017 and the ELKARTEK programs, and by the Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa excellence accreditation SVP-2014-068574 and SEV-2013-0323, and through the project TIN2017-82626-R funded by (AEI/FEDER, UE).
dc.description.statusPeer reviewed
dc.format.extent14
dc.identifier.citationOregi , I , Del Ser , J , Perez , A & Lozano , J A 2018 , Adversarial sample crafting for time series classification with elastic similarity measures . in Studies in Computational Intelligence . Studies in Computational Intelligence , vol. 798 , Springer Verlag , pp. 26-39 . https://doi.org/10.1007/978-3-319-99626-4_3
dc.identifier.doi10.1007/978-3-319-99626-4_3
dc.identifier.issn1860-949X
dc.identifier.urihttps://hdl.handle.net/11556/1699
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85053457377&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherSpringer Verlag
dc.relation.ispartofStudies in Computational Intelligence
dc.relation.ispartofseriesStudies in Computational Intelligence
dc.relation.projectIDAEI/FEDER
dc.relation.projectIDBERC
dc.relation.projectIDAustralian Education International, Australian Government, AEI
dc.relation.projectIDFederación Española de Enfermedades Raras, FEDER
dc.relation.projectIDEusko Jaurlaritza
dc.relation.projectIDMinisterio de Economía y Competitividad, MINECO, TIN2017-82626-R-SEV-2013-0323-SVP-2014-068574
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsAdversarial machine learning
dc.subject.keywordsElastic similarity measures
dc.subject.keywordsTime series classification
dc.subject.keywordsArtificial Intelligence
dc.titleAdversarial sample crafting for time series classification with elastic similarity measuresen
dc.typebook part
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