ASSESSING THE EFFECTIVENESS OF PLAGIARISM DETECTION STRATEGIES: A REVIEW OF CURRENT TECHNIQUES AND FUTURE CHALLENGES

Abstract

Plagiarism is a major problem in academic and research fields that can harm the reputation of individuals and institutions. Consequently, researchers have developed a range of techniques for detecting plagiarism. In this paper, we provide an extensive survey of various plagiarism detection strategies, encompassing monolingual, character-based, vector-based, cross-lingual, syntax-based, fuzzy-based, semantic-based, structure-based, stylometric-based, hybrid-based, and citation-based approaches. Each technique is discussed in detail, including its strengths, weaknesses, and challenges. Additionally, the paper examines recent research works on each technique, focusing on the issues and challenges faced by researchers. The study concludes that no single method is flawless, with each technique presenting its own strengths and weaknesses. A combination of techniques may be required to achieve high accuracy in plagiarism detection. This paper reviews the different techniques used for plagiarism detection and discusses their strengths and limitations, as well as the challenges that still need to be addressed.