Plagiarism detection

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

Mitigation of Rotational Constraints in Image Based Plagiarism Detection Using Perceptual Hash

Abstract: Image plagiarism can be detected using similarity between the two images. The perceptual hash function calculates similar hash value for similar images. This paper proposes a new modification to perceptual hashing (pHash) based approach for Image Retrieval. By the simplified use of a distance function two perceptual hash values can be compared and, it can be understood whether the images being compared are perceptually different or not. Existing Image Plagiarism detection techniques are not sensitive to image rotation. We propose an algorithm that compares the various degrees of rotation of the source image to the perceived similar image in an efficient way. Perceptual image hash functions along with the rotation check can be used for the identification of plagiarised images, authentication or integrity verification of images.

Arabic English Cross-Lingual Plagiarism Detection Based on Keyphrases Extraction, Monolingual and Machine Learning Approach

Abstract: Due to rapid growth of research articles in various languages, cross-lingual plagiarism detection problem has received increasing interest in recent years. Cross-lingual plagiarism detection is more challenging task than monolingual plagiarism detection. This paper addresses the problem of cross-lingual plagiarism detection (CLPD) by proposing a method that combines keyphrases extraction, monolingual detection methods and machine learning approach. The research methodology used in this study has facilitated to accomplish the objectives in terms of designing, developing, and implementing an efficient Arabic – English cross lingual plagiarism detection. This paper empirically evaluates five different monolingual plagiarism detection methods namely i)N-Grams Similarity, ii)Longest Common Subsequence, iii)Dice Coefficient, iv)Fingerprint based Jaccard Similarity and v) Fingerprint based Containment Similarity. In addition, three machine learning approaches namely i) naïve Bayes, ii) Support Vector Machine, and iii) linear logistic regression classifiers are used for Arabic-English Cross-language plagiarism detection. Several experiments are conducted to evaluate the performance of the key phrases extraction methods. In addition, Several experiments to investigate the performance of machine learning techniques to find the best method for Arabic-English Cross-language plagiarism detection. According to the experiments of Arabic-English Cross-language plagiarism detection, the highest result was obtained using SVM classifier with 92% f-measure. In addition, the highest results were obtained by all classifiers are achieved, when most of the monolingual plagiarism detection methods are used.

Plagiarism in Non-Anglophone Countries: a Cross-sectional Survey of Researchers and Journal Editors

Abstract: Background Plagiarism is one of the most common violation of publication ethics, and it still remains an area with several misconceptions and uncertainties. Methods This online cross-sectional survey was conducted to analyze plagiarism perceptions among researchers and journal editors, particularly from non-Anglophone countries. Results Among 211 respondents (mean age 40 years; M:F, 0.85:1), 26 were scholarly journal editors and 70 were reviewers with a large representation from India (50, 24%), Turkey (28, 13%), Kazakhstan (25, 12%) and Ukraine (24, 11%). Rigid and outdated pre- and post-graduate education was considered as the origin of plagiarism by 63% of respondents. Paraphragiarism was the most commonly encountered type of plagiarism (145, 69%). Students (150, 71%), non-Anglophone researchers with poor English writing skills (117, 55%), and agents of commercial editing agencies (126, 60%) were thought to be prone to plagiarize. There was a significant disagreement on the legitimacy of text copying in scholarly articles, permitted plagiarism limit, and plagiarized text in methods section. More than half (165, 78%) recommended specifically designed courses for plagiarism detection and prevention, and 94.7% (200) thought that social media platforms may be deployed to educate and notify about plagiarism. Conclusion Great variation exists in the understanding of plagiarism, potentially contributing to unethical publications and even retractions. Bridging the knowledge gap by arranging topical education and widely employing advanced anti-plagiarism software address this unmet need.

A Comparison of Rabin Karp and Semantic-Based Plagiarism Detection

Abstract: Document plagiarism is a challenging task for scholars.Similarity computation of two documents is the main step ofdocument plagiarism. The accuracy of Rabin Karp andsemantic-based document plagiarism is measured forcomparison. This paper employed Latent Semantic Analysis(LSA) approach via Singular Value Decomposition (SVD) asthe semantic-based document plagiarism. The result showedRabin Karp has better performance than LSA Plagiarism. Proc. of 3rd International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT 2012), Bali, Indonesia, 2012 http://www.academia.edu/5388466/A_Comparison_of_Rabin_Karp_and_Semantic-Based_Plagiarism_Detection

Design a Persian Automated Plagiarism Detector (AMZPPD)

Abstract: Currently there are lots of plagiarism detection approaches. But few of them implemented and adapted for Persian languages. In this paper, our work on designing and implementation of a plagiarism detection system based on pre-processing and NLP technics will be described. And the results of testing on a corpus will be presented.