Plagiarism detection in texts obfuscated with homoglyphs

Faisal Alvi; Mark Stevenson; Paul Clough

doi:10.1007/978-3-319-56608-5_64

Plagiarism detection in texts obfuscated with homoglyphs

Faisal Alvi^*, Mark Stevenson, Paul Clough

^*Corresponding author for this work

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Homoglyphs can be used for disguising plagiarized text by replacing letters in source texts with visually identical letters from other scripts. Most current plagiarism detection systems are not able to detect plagiarism when text has been obfuscated using homoglyphs. In this work, we present two alternative approaches for detecting plagiarism in homoglyph obfuscated texts. The first approach utilizes the Unicode list of confusables to replace homoglyphs with visually identical letters, while the second approach uses a similarity score computed using normalized hamming distance to match homoglyph obfuscated words with source words. Empirical testing on datasets from PAN-2015 shows that both approaches perform equally well for plagiarism detection in homoglyph obfuscated texts.

Original language	English
Title of host publication	Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings
Editors	Claudia Hauff, Joemon M. Jose, Dyaa Albakour, Ismail Sengor Altingovde, John Tait, Dawei Song, Stuart Watt
Publisher	Springer Verlag
Pages	669-675
Number of pages	7
ISBN (Print)	9783319566078
DOIs	https://doi.org/10.1007/978-3-319-56608-5_64
State	Published - 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10193 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-319-56608-5_64

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Alvi, F., Stevenson, M., & Clough, P. (2017). Plagiarism detection in texts obfuscated with homoglyphs. In C. Hauff, J. M. Jose, D. Albakour, I. S. Altingovde, J. Tait, D. Song, & S. Watt (Eds.), Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings (pp. 669-675). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10193 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-56608-5_64

Alvi, Faisal ; Stevenson, Mark ; Clough, Paul. / Plagiarism detection in texts obfuscated with homoglyphs. Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings. editor / Claudia Hauff ; Joemon M. Jose ; Dyaa Albakour ; Ismail Sengor Altingovde ; John Tait ; Dawei Song ; Stuart Watt. Springer Verlag, 2017. pp. 669-675 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Homoglyphs can be used for disguising plagiarized text by replacing letters in source texts with visually identical letters from other scripts. Most current plagiarism detection systems are not able to detect plagiarism when text has been obfuscated using homoglyphs. In this work, we present two alternative approaches for detecting plagiarism in homoglyph obfuscated texts. The first approach utilizes the Unicode list of confusables to replace homoglyphs with visually identical letters, while the second approach uses a similarity score computed using normalized hamming distance to match homoglyph obfuscated words with source words. Empirical testing on datasets from PAN-2015 shows that both approaches perform equally well for plagiarism detection in homoglyph obfuscated texts.",

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Alvi, F, Stevenson, M & Clough, P 2017, Plagiarism detection in texts obfuscated with homoglyphs. in C Hauff, JM Jose, D Albakour, IS Altingovde, J Tait, D Song & S Watt (eds), Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10193 LNCS, Springer Verlag, pp. 669-675. https://doi.org/10.1007/978-3-319-56608-5_64

Plagiarism detection in texts obfuscated with homoglyphs. / Alvi, Faisal; Stevenson, Mark; Clough, Paul.
Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings. ed. / Claudia Hauff; Joemon M. Jose; Dyaa Albakour; Ismail Sengor Altingovde; John Tait; Dawei Song; Stuart Watt. Springer Verlag, 2017. p. 669-675 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10193 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Plagiarism detection in texts obfuscated with homoglyphs

AU - Alvi, Faisal

AU - Stevenson, Mark

AU - Clough, Paul

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Homoglyphs can be used for disguising plagiarized text by replacing letters in source texts with visually identical letters from other scripts. Most current plagiarism detection systems are not able to detect plagiarism when text has been obfuscated using homoglyphs. In this work, we present two alternative approaches for detecting plagiarism in homoglyph obfuscated texts. The first approach utilizes the Unicode list of confusables to replace homoglyphs with visually identical letters, while the second approach uses a similarity score computed using normalized hamming distance to match homoglyph obfuscated words with source words. Empirical testing on datasets from PAN-2015 shows that both approaches perform equally well for plagiarism detection in homoglyph obfuscated texts.

AB - Homoglyphs can be used for disguising plagiarized text by replacing letters in source texts with visually identical letters from other scripts. Most current plagiarism detection systems are not able to detect plagiarism when text has been obfuscated using homoglyphs. In this work, we present two alternative approaches for detecting plagiarism in homoglyph obfuscated texts. The first approach utilizes the Unicode list of confusables to replace homoglyphs with visually identical letters, while the second approach uses a similarity score computed using normalized hamming distance to match homoglyph obfuscated words with source words. Empirical testing on datasets from PAN-2015 shows that both approaches perform equally well for plagiarism detection in homoglyph obfuscated texts.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings

A2 - Hauff, Claudia

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PB - Springer Verlag

ER -

Alvi F, Stevenson M, Clough P. Plagiarism detection in texts obfuscated with homoglyphs. In Hauff C, Jose JM, Albakour D, Altingovde IS, Tait J, Song D, Watt S, editors, Advances in Information Retrieval - 39th European Conference on IR Research, ECIR 2017, Proceedings. Springer Verlag. 2017. p. 669-675. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-56608-5_64

Plagiarism detection in texts obfuscated with homoglyphs

Abstract

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Bibliographical note

ASJC Scopus subject areas

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