Stay On-Topic

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorJuuti, Mikaen_US
dc.contributor.authorSun, Boen_US
dc.contributor.authorMori, Tatsuyaen_US
dc.contributor.authorAsokan, N.en_US
dc.contributor.departmentAdj. Prof Asokan N. groupen_US
dc.contributor.departmentWaseda Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen
dc.date.accessioned2019-01-14T09:19:54Z
dc.date.available2019-01-14T09:19:54Z
dc.date.issued2018en_US
dc.description.abstractAutomatically generated fake restaurant reviews are a threat to online review systems. Recent research has shown that users have difficulties in detecting machine-generated fake reviews hiding among real restaurant reviews. The method used in this work (char-LSTM ) has one drawback: it has difficulties staying in context, i.e. when it generates a review for specific target entity, the resulting review may contain phrases that are unrelated to the target, thus increasing its detectability. In this work, we present and evaluate a more sophisticated technique based on neural machine translation (NMT) with which we can generate reviews that stay on-topic. We test multiple variants of our technique using native English speakers on Amazon Mechanical Turk. We demonstrate that reviews generated by the best variant have almost optimal undetectability (class-averaged F-score 47%). We conduct a user study with skeptical users and show that our method evades detection more frequently compared to the state-of-the-art (average evasion 3.2/4 vs 1.5/4) with statistical significance, at level {\alpha} = 1% (Section 4.3). We develop very effective detection tools and reach average F-score of 97% in classifying these. Although fake reviews are very effective in fooling people, effective automatic detection is still feasible.en
dc.description.versionPeer revieweden
dc.format.extent20
dc.format.extent132-151
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationJuuti , M , Sun , B , Mori , T & Asokan , N 2018 , Stay On-Topic : Generating Context-specific Fake Restaurant Reviews . in Computer Security - 23rd European Symposium on Research in Computer Security, ESORICS 2018, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 11098 LNCS , Springer , pp. 132-151 , European Symposium on Research in Computer Security , Barcelona , Spain , 03/09/2018 . https://doi.org/10.1007/978-3-319-99073-6_7en
dc.identifier.doi10.1007/978-3-319-99073-6_7en_US
dc.identifier.isbn9783319990729
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.otherPURE UUID: 2970e6a4-7c62-4907-a215-9346e76ed4daen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/2970e6a4-7c62-4907-a215-9346e76ed4daen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/31096425/SCI_Juuti_Sun_Stay_On_Topic.esorics_camera.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/35928
dc.identifier.urnURN:NBN:fi:aalto-201901141111
dc.language.isoenen
dc.publisherSpringer
dc.relation.ispartofEuropean Symposium on Research in Computer Securityen
dc.relation.ispartofseriesComputer Security - 23rd European Symposium on Research in Computer Security, ESORICS 2018, Proceedingsen
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.relation.ispartofseriesVolume 11098 LNCSen
dc.rightsopenAccessen
dc.subject.keywordsecurityen_US
dc.subject.keywordMachine Learningen_US
dc.subject.keywordfraud detectionen_US
dc.subject.keywordneural machine translationen_US
dc.subject.keywordsocial mediaen_US
dc.subject.keywordnatural languageen_US
dc.titleStay On-Topicen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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