Topic
Plagiarism detection
About: Plagiarism detection is a research topic. Over the lifetime, 1790 publications have been published within this topic receiving 24740 citations.
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01 Aug 2015
TL;DR: The results demonstrate that the performance of the proposed fuzzy-based approach overcomes all other approaches on well-known source code datasets, and reveals promising results as an efficient and reliable approach to source-code plagiarism detection.
Abstract: Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. Fuzzy clustering approaches are a suitable solution to detecting source-code plagiarism due to their capability to capture the qualitative and semantic elements of similarity. This paper proposes a novel Fuzzy-based approach to source-code plagiarism detection, based on Fuzzy C-Means and the Adaptive-Neuro Fuzzy Inference System (ANFIS). In addition, performance of the proposed approach is compared to the Self- Organising Map (SOM) and the state-of-the-art plagiarism detection Running Karp-Rabin Greedy-String-Tiling (RKR-GST) algorithms. The advantages of the proposed approach are that it is programming language independent, and hence there is no need to develop any parsers or compilers in order for the fuzzy-based predictor to provide detection in different programming languages. The results demonstrate that the performance of the proposed fuzzy-based approach overcomes all other approaches on well-known source code datasets, and reveals promising results as an efficient and reliable approach to source-code plagiarism detection.
30 citations
20 Sep 2010
TL;DR: A plagiarism detection method composed by five main phases: language normalization, retrieval of candidate documents, classifier training, plagiarism analysis, and post-processing, showing that the method achieved better results with medium and large plagiarized passages.
Abstract: This paper presents a new method for Cross-Language Plagiarism Analysis. Our task is to detect the plagiarized passages in the suspicious documents and their corresponding fragments in the source documents. We propose a plagiarism detection method composed by five main phases: language normalization, retrieval of candidate documents, classifier training, plagiarism analysis, and post-processing. To evaluate our method, we created a corpus containing artificial plagiarism offenses. Two different experiments were conducted; the first one considers only monolingual plagiarism cases, while the second one considers only cross-language plagiarism cases. The results showed that the cross-language experiment achieved 86% of the performance of the monolingual baseline. We also analyzed how the plagiarized text length affects the overall performance of the method. This analysis showed that our method achieved better results with medium and large plagiarized passages.
30 citations
01 Oct 2016
TL;DR: Various techniques and algorithms to discover plagiarism in source code using these techniques will be explained and differentiated among these given techniques to discover how one technique is conflicting with the other.
Abstract: Plagiarism is becoming a serious problem for intellectual community. The detection of plagiarism at various levels is a major issue. The complexity of the problem increases when we are finding the plagiarism in the source codes that may be in the same language or they have been transformed into other languages. This type of plagiarism is found not only in the academic works but also in the industries dealing with software designing. The major issue with the source code plagiarism is that different programming languages may have different syntax. In this paper the authors will explain various techniques and algorithms to discover the plagiarism in source code. So organization or academic institution can simply discover plagiarism in source code using these techniques. The authors will differentiate among these given techniques of plagiarism to discover how one technique is conflicting with the other.
30 citations
01 Jan 2015
TL;DR: This paper overviews the five source retrieval approaches that have been submitted to the seventh international competition on plagiarism detection at PAN 2015 and compares the performances of these five approaches to the 14 methods submitted in the two previous years.
Abstract: This paper overviews the five source retrieval approaches that have been submitted to the seventh international competition on plagiarism detection at PAN 2015. We compare the performances of these five approaches to the 14 methods submitted in the two previous years (eight from PAN 2013 and six from PAN 2014). For the third year in a row, we invited software submissions instead of run submissions, such that cross-year evaluations are possible. This year’s stand-alone source retrieval overview can thus to some extent also be used as a reference to the different ideas presented in the last three years—the text alignment subtask will be depicted in another individual overview. Linda Cappellato and Nicola Ferro and Gareth Jones and Eric San Juan (eds.): CLEF 2015 Labs and Workshops, Notebook Papers, 8-11 September, Toulouse, France. CEUR Workshop Proceedings. ISSN 1613-0073, http://ceur-ws.org/Vol-1391/, 2015.
30 citations
TL;DR: Overall, institutional policies on self-plagiarism did not exist and faculty did not clearly understand the concept and believed their students did not either, and faculty assumed students had previously been educated on plagiarism as well as self-PLAGiarism.
Abstract: The purpose of this research study was to evaluate faculty perceptions regarding student self-plagiarism or recycling of student papers. Although there is a plethora of information on plagiarism and faculty who self-plagiarize in publications, there is very little research on how faculty members perceive students re-using all or part of a previously completed assignment in a second assignment. With the wide use of plagiarism detection software, this issue becomes even more crucial. A population of 340 faculty members from two private universities at three different sites was surveyed in Fall 2012 semester regarding their perceptions of student self-plagiarism. A total of 89 faculty responded for a return rate of 26.2 %. Overall, institutional policies on self-plagiarism did not exist and faculty did not clearly understand the concept and believed their students did not either. Although faculty agreed students need to be educated on self-plagiarism, faculty assumed students had previously been educated on plagiarism as well as self-plagiarism; only 13 % ensured students understood this concept.
30 citations