Stay on-topic: Generating context-specific fake restaurant reviews

Mika Juuti, Bo Sun, Tatsuya Mori, N. Asokan

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Automatically 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 experienced 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 α1% (Sect. 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.

本文言語English
ホスト出版物のタイトルComputer Security - 23rd European Symposium on Research in Computer Security, ESORICS 2018, Proceedings
編集者Javier Lopez, Jianying Zhou, Miguel Soriano
出版社Springer Verlag
ページ132-151
ページ数20
ISBN(印刷版)9783319990729
DOI
出版ステータスPublished - 2018
イベント23rd European Symposium on Research in Computer Security, ESORICS 2018 - Barcelona, Spain
継続期間: 2018 9 32018 9 7

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11098 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other23rd European Symposium on Research in Computer Security, ESORICS 2018
CountrySpain
CityBarcelona
Period18/9/318/9/7

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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