Intelligent Plagiarism Detection for Electronic Documents

Plagiarism detection is the process of finding similarities on electronic based documents. Recently, this process is highly required because of the large number of available documents on the internet and the ability to copy and paste the text of relevant documents with simply Control+C and Control+V commands. The proposed solution is to investigate and develop an easy, fast, and multi-language support plagiarism detector with the easy of one click to detect the document plagiarism. This process will be done with the support of intelligent system that can learn, change and adapt to the input document and make a cross-fast search for the content on the local repository and the online repository and link the content of the file with the matching content everywhere found. Furthermore, the supported document type that we will use is word, text and in some cases, the pdf files –where is the text can be extracting from them- and this made possible by using the DLL file from Word application that Microsoft provided on OS. The using of DLL will let us to not constrain on how to get the text from files; and will help us to apply the file on our Delphi project and walk throw our methodology and read the file word by word to grantee the best working scenarios for the calculation. In the result, this process will help in the uprising the documents quality and enhance the writer experience related to his work and will save the copyrights for the official writer of the documents by providing a new alternative tool for plagiarism detection problem for easy and fast use to the concerned Institutions for free

AntiPlag: Plagiarism Detection on Electronic Submissions of Text Based Assignments

Plagiarism is one of the growing issues in academia and is always a concern in Universities and other academic institutions. The situation is becoming even worse with the availability of ample resources on the web. This paper focuses on creating an effective and fast tool for plagiarism detection for text based electronic assignments. Our plagiarism detection tool named AntiPlag is developed using the tri-gram sequence matching technique. Three sets of text based assignments were tested by AntiPlag and the results were compared against an existing commercial plagiarism detection tool. AntiPlag showed better results in terms of false positives compared to the commercial tool due to the pre-processing steps performed in AntiPlag. In addition, to improve the detection latency, AntiPlag applies a data clustering technique making it four times faster than the commercial tool considered. AntiPlag could be used to isolate plagiarized text based assignments from non-plagiarised assignments easily. Therefore, we present AntiPlag, a fast and effective tool for plagiarism detection on text based electronic assignments

Spot the Difference! Visual plagiarism in the visual arts.

Over recent years there has been considerable investment in the use of technology to identify sources of text-based plagiarism in universities. However, students of the visual arts are also required to complete numerous pieces of visual submissions for assessment, and yet very little similar work has been undertaken in the area of non-text based plagiarism detection. The Spot the Difference! project (2011-2012), funded by JISC and led by the University for the Creative Arts, seeks to address this gap by piloting the use of visual search tools developed by the University of Surrey and testing their application to support learning and teaching in the arts and specifically to the identification of visual plagiarism. Given that most commonly used search technologies rely on text, the identification and evidencing of visual plagiarism is often left to the knowledge and experience of academic staff, which can potentially result in inconsistency of detection, approach, policies and practices. This paper outlines the work of the project team, who sought to investigate the nature, scope and extent of visual plagiarism in the arts education sector

Tackling the PAN’09 External Plagiarism Detection Corpus with a Desktop Plaigiarism Detector

Ferret is a fast and effective tool for detecting similarities in a group of files. Applying it to the PAN’09 corpus required modifications to meet the requirements of the competition, mainly to deal with the very large number of files, the large size of some of them, and to automate some of the decisions that would normally be made by a human operator. Ferret was able to detect numerous files in the development corpus that contain substantial similarities not marked as plagiarism, but it also identified quite a lot of pairs where random similarities masked actual plagiarism. An improved metric is therefore indicated if the “plagiarised” or “not plagiarised” decision is to be automated