Adversarial Machine Learning for Protecting Against Online Manipulation

Cite This

Files in this item

Files Size Format View

There are no files associated with this item.

CRESCI, Stefano, Marinella PETROCCHI, Angelo SPOGNARDI, Stefano TOGNAZZI, 2022. Adversarial Machine Learning for Protecting Against Online Manipulation. In: IEEE Internet Computing. IEEE. 26(2), pp. 47-52. ISSN 1089-7801. eISSN 1941-0131. Available under: doi: 10.1109/MIC.2021.3130380

@article{Cresci2022Adver-57836, title={Adversarial Machine Learning for Protecting Against Online Manipulation}, year={2022}, doi={10.1109/MIC.2021.3130380}, number={2}, volume={26}, issn={1089-7801}, journal={IEEE Internet Computing}, pages={47--52}, author={Cresci, Stefano and Petrocchi, Marinella and Spognardi, Angelo and Tognazzi, Stefano} }

<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/rdf/resource/123456789/57836"> <dc:creator>Tognazzi, Stefano</dc:creator> <dc:creator>Cresci, Stefano</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Petrocchi, Marinella</dc:creator> <dcterms:abstract xml:lang="eng">Adversarial examples are inputs to a machine learning system that result in an incorrect output from that system. Attacks launched through this type of input can cause severe consequences: for example, in the field of image recognition, a stop signal can be misclassified as a speed limit indication. However, adversarial examples also represent the fuel for a flurry of research directions in different domains and applications. Here, we give an overview of how they can be profitably exploited as powerful tools to build stronger learning models, capable of better-withstanding attacks, for two crucial tasks: fake news and social bot detection.</dcterms:abstract> <dc:contributor>Tognazzi, Stefano</dc:contributor> <dcterms:issued>2022</dcterms:issued> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:contributor>Petrocchi, Marinella</dc:contributor> <dc:contributor>Cresci, Stefano</dc:contributor> <dc:language>eng</dc:language> <dc:creator>Spognardi, Angelo</dc:creator> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/43615"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:contributor>Spognardi, Angelo</dc:contributor> <dcterms:title>Adversarial Machine Learning for Protecting Against Online Manipulation</dcterms:title> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-06-23T13:50:39Z</dc:date> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-06-23T13:50:39Z</dcterms:available> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/43615"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/57836"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> </rdf:Description> </rdf:RDF>

This item appears in the following Collection(s)

Search KOPS


Browse

My Account