Plagiarism detection

About: Plagiarism detection is a research topic. Over the lifetime, 1790 publications have been published within this topic receiving 24740 citations.

Software plagiarism detection: a graph-based approach

Abstract: As plagiarism of software increases rapidly, there are growing needs for software plagiarism detection systems. In this paper, we propose a software plagiarism detection system using an API-labeled control flow graph (A-CFG) that abstracts the functionalities of a program. The A-CFG can reflect both the sequence and the frequency of APIs, while previous work rarely considers both of them together. To perform a scalable comparison of a pair of A-CFGs, we use random walk with restart (RWR) that computes an importance score for each node in a graph. By the RWR, we can generate a single score vector for an A-CFG and can also compare A-CFGs by comparing their score vectors. Extensive evaluations on a set of Windows applications demonstrate the effectiveness and the scalability of our proposed system compared with existing methods.

Sentence-based natural language plagiarism detection

Abstract: With the increasing levels of access to higher education in the United Kingdom, larger class sizes make it unrealistic for tutors to be expected to identify instances of peer-to-peer plagiarism by eye and so automated solutions to the problem are required. This document details a novel algorithm for comparison of suspect documents at a sentence level and has been implemented as a component of plagiarism detection software for detecting similarities in both natural language documents and comments within program source-code. The algorithm is capable of detecting sophisticated obfuscation (such as paraphrasing, reordering, merging, and splitting sentences) as well as direct copying. The implemented algorithm has also been used to successfully detect plagiarism on real assignments at the university. The software has been evaluated by comparison with other plagiarism detection tools.

Overview and Comparison of Plagiarism Detection Tools

Abstract: In this paper we have done an overview of eective plagiarism detection methods that have been used for natural language text plagia- rism detection, external plagiarism detection, clustering-base plagiarism detection and some methods used in code source plagiarism detection, also we have done a comparison between five of software used for tex- tual plagiarism detection: (PlagAware, PlagScan, Check for Plagiarism, iThenticate and PlagiarismDetection.org), software are compared with respect of their features and performance.

Study on extrinsic text plagiarism detection techniques and tools

Abstract: The swift evolution of technology has facilitated the access of information through different means which has opened the doors to plagiarism. In today’s world of technological outburst, plagiarism is aggravating and has become a serious concern in academia, research and many other fields. To curb this intellectual theft and to ensure academic integrity, efficient software systems to detect them are in urgent need. In this paper, a study on plagiarism is done with the focus on extrinsic text plagiarism detection, which is a fast emerging research area in this domain. The different extrinsic detection techniques and the methodologies involved are reviewed based on the current state of art. Further an overview of some of the available detection software tools, their features and detection efficiency is discussed with some of the output demos. The paper also throws light on the popular PAN competition, which is conducted yearly since 2009 in plagiarism domain and the major tasks involved in it. Further it attempts to identify the problems existing in available tools and the research gaps where immense explorations can be done.

PDLK: Plagiarism detection using linguistic knowledge

Abstract: Plagiarism is described as the reuse of someone else's previous ideas, work or even words without sufficient attribution to the source. This paper presents a method to detect external plagiarism using the integration of semantic relations between words and their syntactic composition. The problem with the available methods is that they fail to capture the meaning in comparison between a source document sentence and a suspicious document sentence, when two sentences have same surface text (the words are the same) or they are a paraphrase of each other. Therefore it causes inaccurate or unnecessary matching results. However, this method can improve the performance of plagiarism detection because it is able to avoid selecting the source text sentence whose similarity with suspicious text sentence is high but its meaning is different. It is executed by computing the semantic and syntactic similarity of the sentence-to-sentence. Besides, the proposed method expands the words in sentences to tackle the problem of information limit. It bridges the lexical gaps for semantically similar contexts that are expressed in a different wording. This method is also capable to identify various kinds of plagiarism such as the exact copied text, paraphrasing, transformation of sentences and changing of word structure in the sentences. As a result, the experimental results have displayed that the proposed method is able to improve the performance compared with the participating systems in PAN-PC-11. The experimental results also displayed that the proposed method demonstrates better performance as compared to other existing techniques on PAN-PC-10 and PAN-PC-11 datasets.