Publikation:

Verifying Global Neural Network Specifications using Hyperproperties

Lade...
Vorschaubild

Dateien

Boetius_2-vqdbjrm55ool7.pdf
Boetius_2-vqdbjrm55ool7.pdfGröße: 708.13 KBDownloads: 20

Datum

2023

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

DOI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Link zur Lizenz
oops

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published

Erschienen in

NARODYTSKA, Nina, Hrsg., Guy AMIR, Hrsg., Guy KATZ, Hrsg. und andere. Proceedings of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems : FoMLAS2023. EasyChair, 2023. Kalpa Publications in Computing. 16. ISSN 2515-1762. Verfügbar unter: doi: 10.29007/pvtn

Zusammenfassung

Current approaches to neural network verification focus on specifications that target small regions around known input data points, such as local robustness. Thus, using these approaches, we can not obtain guarantees for inputs that are not close to known inputs. Yet, it is highly likely that a neural network will encounter such truly unseen inputs during its application. We study global specifications that — when satisfied — provide guarantees for all potential inputs. We introduce a hyperproperty formalism that allows for expressing global specifications such as monotonicity, Lipschitz continuity, global robustness, and dependency fairness. Our formalism enables verifying global specifications using existing neural network verification approaches by leveraging capabilities for verifying general computational graphs. Thereby, we extend the scope of guarantees that can be provided using existing methods. Recent success in verifying specific global specifications shows that attaining strong guarantees for all potential data points is feasible.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

6th Workshop on Formal Methods for ML-Enabled Autonomous Systems : FoMLAS2023, 17. Juli 2023 - 18. Juli 2023, Paris, France
Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690BOETIUS, David, Stefan LEUE, 2023. Verifying Global Neural Network Specifications using Hyperproperties. 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems : FoMLAS2023. Paris, France, 17. Juli 2023 - 18. Juli 2023. In: NARODYTSKA, Nina, Hrsg., Guy AMIR, Hrsg., Guy KATZ, Hrsg. und andere. Proceedings of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems : FoMLAS2023. EasyChair, 2023. Kalpa Publications in Computing. 16. ISSN 2515-1762. Verfügbar unter: doi: 10.29007/pvtn
BibTex
@inproceedings{Boetius2023Verif-70511,
  year={2023},
  doi={10.29007/pvtn},
  title={Verifying Global Neural Network Specifications using Hyperproperties},
  number={16},
  issn={2515-1762},
  publisher={EasyChair},
  series={Kalpa Publications in Computing},
  booktitle={Proceedings of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems : FoMLAS2023},
  editor={Narodytska, Nina and Amir, Guy and Katz, Guy},
  author={Boetius, David and Leue, Stefan}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/70511">
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-07-31T10:41:12Z</dc:date>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Boetius, David</dc:creator>
    <dcterms:abstract>Current approaches to neural network verification focus on specifications that target small regions around known input data points, such as local robustness. Thus, using these approaches, we can not obtain guarantees for inputs that are not close to known inputs. Yet, it is highly likely that a neural network will encounter such truly unseen inputs during its application. We study global specifications that — when satisfied — provide guarantees for all potential inputs. We introduce a hyperproperty formalism that allows for expressing global specifications such as monotonicity, Lipschitz continuity, global robustness, and dependency fairness. Our formalism enables verifying global specifications using existing neural network verification approaches by leveraging capabilities for verifying general computational graphs. Thereby, we extend the scope of guarantees that can be provided using existing methods. Recent success in verifying specific global specifications shows that attaining strong guarantees for all potential data points is feasible.</dcterms:abstract>
    <dcterms:issued>2023</dcterms:issued>
    <dcterms:title>Verifying Global Neural Network Specifications using Hyperproperties</dcterms:title>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/70511/1/Boetius_2-vqdbjrm55ool7.pdf"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/70511/1/Boetius_2-vqdbjrm55ool7.pdf"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/70511"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:language>eng</dc:language>
    <dc:contributor>Boetius, David</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Leue, Stefan</dc:creator>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2024-07-31T10:41:12Z</dcterms:available>
    <dc:contributor>Leue, Stefan</dc:contributor>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen