Determining the direction of plagiarism (who plagiarized whom in a given pair of documents) is one of the most interesting problems in the field of automatic plagiarism detection. We present here an approach using an extension of the method Encoplot, which won the 1st international competition on plagiarism detection in 2009. We have tested it on a large-scale corpus of artificial plagiarism, w...

In a previous paper, we showed that analyzing citation patterns in the well-known plagiarized thesis by K. T. zu Guttenberg clearly outperformed current detection methods in identifying cross-language plagiarism. However, the experiment was a proof of concept and we did not provide a prototype. This paper presents a fully functional, web-based visualization of citation patterns for this verifie...

We present an evaluation framework for plagiarism detection.1 The framework provides performance measures that address the specifics of plagiarism detection, and the PAN-PC-10 corpus, which contains 64 558 artificial and 4 000 simulated plagiarism cases, the latter generated via Amazon’s Mechanical Turk. We discuss the construction principles behind the measures and the corpus, and we compare t...

This paper outlines the main approach and the general design of the plagiarism detection prototype application we have developed to take part in the 2nd International Plagiarism Detection Competition. The developed system is a part of the larger application used at Zhytomyr State University as CMS Thesis Storage and comes under the title "Plagiarism Detector Accumulator". This application proto...

Plagiarism is a common place in academics, especially in courses involving programming. In this paper, XPDec, an XML-based model is introduced to detect similarities among programs that arise under plagiarism. Based upon the syntax of a specific programming language, XPDec uses an XML scheme that is suitable for the detection of plagiarism. XML documents are generated from given program sources...

Detecting academic plagiarism is a pressing problem, e.g., for educational and research institutions, funding agencies, and academic publishers. Existing plagiarism detection systems reliably identify (nearly) copied text, but o‰en fail to detect disguised forms of academic plagiarism, such as paraphrases, translations, and idea plagiarism. We present Semantic Concept PaŠern Analysis an approac...

The availability and use of computers in teaching has seen an increase in the rate of plagiarism among students because of the wide availability of electronic texts online. While computer tools that have appeared in recent years are capable of detecting simple forms of plagiarism, such as copy-paste, a number of recent research studies devoted to evaluation and comparison of plagiarism detectio...

We proposed an Architectural model for detecting plagiarism in natural language text and presented the analysis of various detection processes followed for effective plagiarism detection. Other plagiarism detection mechanisms are based on parsing techniques where sentence and word chunking is performed to extract phrases which are searched on internet in comparison to that we performed sentence...

This paper overviews 17 plagiarism detectors that have been evaluated within the sixth international competition on plagiarism detection at PAN 2014. We report on their performances for the two tasks source retrieval and text alignment of external plagiarism detection. For the third year in a row, we invite software submissions instead of run submissions for this task, which allows for cross-ye...

AraPlagDet is the first shared task that addresses the evaluation of plagiarism detection methods for Arabic texts. It has two subtasks, namely external plagiarism detection and intrinsic plagiarism detection. A total of 8 runs have been submitted and tested on the standardized corpora developed for the track. This overview paper describes these evaluation corpora, discusses the participants’ m...