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TurnItOff - Defeating Plagiarism Detection Systems

Gillam, L, Marinuzzi, J and Ioannou, P (2010) TurnItOff - Defeating Plagiarism Detection Systems In: 11th Higher Education Academy-ICS Annual Conference, 2010-08-24 - 2010-08-26, University of Durham.

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Abstract

Defeating plagiarism detection systems involves determining effective approaches for greatest impact at lowest cost with the least likelihood of detection. Relatively simple techniques have been applied elsewhere for avoiding plagiarism detection, demonstrated at the last HEA-ICS conference. In this paper, we discuss defeats for seven plagiarism detection systems, including Essayrater, Seesources, PlagiarismDetector, and the popular Turnitin. We report on initial results of human experiments undertaken on visual similarity to assess the risk of human detection of changes. The systems evaluated are variously susceptible to sufficient numbers of small alterations to characters or words in the text Our results suggest, at minimum, to use at least 2 such systems in combination to reduce the likelihood of failed detection and increase the difficulty for the determined, and yet somehow lazy, plagiarist – otherwise, the discovery and dissemination of simple defeats for plagiarism detection software may mean that we may as well just “Turnitoff”.

Item Type: Conference or Workshop Item (Conference Paper)
Subjects : Computing
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Gillam, LUNSPECIFIEDUNSPECIFIED
Marinuzzi, JUNSPECIFIEDUNSPECIFIED
Ioannou, PUNSPECIFIEDUNSPECIFIED
Date : 26 August 2010
Uncontrolled Keywords : Plagiarism, detection, defeat, visual similarity, risk
Depositing User : Symplectic Elements
Date Deposited : 06 Mar 2017 14:41
Last Modified : 06 Mar 2017 14:41
URI: http://epubs.surrey.ac.uk/id/eprint/790662

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