Review on Various Tools and Techniques for Plagiarism Detection

Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, 2020

With the explosive growth of technology and the easy availability of content on the web, it creates new challenges to discriminate against the original work from plagiarized material. Content is said to be plagiarized when it is taken from other original sources without giving its reference. To address this issue Plagiarism detection tools are required. Over the years, extensive work has been done in the development of anti-plagiarism tools. This paper presents the types of plagiarism with an aim to review Extrinsic Plagiarism detection techniques using Linguistic-based features, Syntactic-based features, and Semantic-based features. Further, an overview of some current state of art methodologies and their results has been discussed on the dataset of PAN-PC 2009, PAN-PC 2010, and PAN-PC 2011. This paper also analyzes the pros and cons of some existing systems and by comparing results it also identifies that some of the systems have less potency to detect the manual and highly shuffled complex types of plagiarism such as translation obfuscation. Keywords Plagiarism detection • Extrinsic plagiarism detection • Intrinsic plagiarism detection • PAN-PC datasets 1 Introduction World Wide Web provides access to data present in the documents, databases, and other sources of information using internet service. The availability of knowledge and information in the digital form leads to "Plagiarism" by "Plagiarist". Plagiarism

ArXiv, 2018

To detect plagiarism of any form, it is essential to have broad knowledge of its possible forms and classes, and existence of various tools and systems for its detection. Based on impact or severity of damages, plagiarism may occur in an article or in any production in a number of ways. This survey presents a taxonomy of various plagiarism forms and include discussion on each of these forms. Over the years, a good number tools and techniques have been introduced to detect plagiarism. This paper highlights few promising methods for plagiarism detection based on machine learning techniques. We analyse the pros and cons of these methods and finally we highlight a list of issues and research challenges related to this evolving research problem.

International Journal of Scientific Research in Science and Technology, 2019

Being a developing issue, plagiarism is commonly described as literature theft and academic dishonest nature in the writing, and it must be avoided and adhere to the moral standards. Plagiarism occur in scholastics, paper publication, music, work of art developing quickly, so the recognizing plagiarism is essential. While the most recent couple of year's plagiarism detection tools have been utilized predominantly in research conditions, refined plagiarism programming and instruments are presently quickly rising. In this paper, we give an outline of various plagiarism programming and apparatuses to take care of the plagiarism issue. We propose an element classification conspire that can be utilized to examine plagiarism discovery programming and plagiarism recognition instruments. This plan depends on the product's general qualities, devices qualities, and apparatuses property.

Plagiarism is still a difficult problem. Many tools have been developed to detect plagiarism. This paper defines plagiarism, shows the benefit of it and declares twenty one different plagiarism service tools provided on the Internet in order to detect the plagiarism process.

Plagiarism is of digital documents seems a serious problem in today's world. Plagiarism refers to the use of others data, language and writing without proper references of the original source. Plagiarism of other author's original work is one of the big problems in publishing, science, and education. Plagiarism can be of various types. Present systems are based on the traditional approach. For verifying plagiarism, traditional methods focus on text or data matching according to keywords but fail to find brilliant plagiarism using correct web. We have suggested new strategies for finding the plagiarism in the user data using the correct web. In paper we have proposed architecture and algorithms to good finding of carbon copy using correct search, it can improve the speed of carbon copy finding system. This is verifying the user data. After the performing of this approach, the perfection of plagiarism finding system will definitely increase.

International Journal of Computer Engineering and Applications, 2021

Literature is an intellectual knowledge and new arguments are being made for the theft of that literature. New technical tools are being created for this problem. It's said that "Money can be stolen, some goods can be stolen but knowledge cannot be stolen". But by stealing the same knowledge as literature, the theft of the same literature begins with writing on paper. In recent years, many online tools have been able to identify potential plagiarism in research areas. In this paper major contents are the dimensions and techniques of plagiarism, NLP problems of plagiarism identifier, problem of sentences.

2022

Plagiarism is becoming more of a problem in academics. It's made worse by the ease with which a wide range of resources can be found on the internet, as well as the ease with which they can be copied and pasted. It is academic theft since the perpetrator has "taken" and presented the work of others as his or her own. Manual detection of plagiarism by a human being is difficult, imprecise, and time-consuming because it is difficult for anyone to compare their work to current data. Plagiarism is a big problem in higher education, and it can happen on any topic. Plagiarism detection has been studied in many scientific articles, and methods for recognition have been created utilizing the Plagiarism analysis, Authorship identification, and Near-duplicate detection (PAN) Dataset 2009-2011. Verbatim plagiarism, according to the researchers, plagiarism is simply copying and pasting. They then moved on to smart plagiarism, which is more challenging to spot since it might include text change, taking ideas from other academics, and translation into a more difficult-to-manage language. Other studies have found that plagiarism can obscure the scientific content of publications by swapping words, removing or adding material, or reordering or changing the original articles. This article discusses the comparative study of plagiarism detection techniques.

iadis.net

Ahmad Zaki Abu Bakar Roliana Ibrahim

Academic dishonesty is one of the critical measures to evaluate research papers, theses and students' assignments. Therefore, plagiarism detection is an area of concern for many researchers especially in the academic field. Other fields such as plagiarized news, magazine articles and web resources are also area of concern. In that regard, many detection techniques and tools have been developed to address the problem of plagiarism. Different types of texts require different techniques to detect plagiarism. Documents to be ...

2021

Plagiarism detection software has become need of the hour. Academicians need to acknowledge this fact and should take some steps towards this new change towards research ethics and reliability. In this research paper, researchers attempt to cover the concept of plagiarism and analyses some of the plagiarism detection software with their features and functions, like-pricing and availability, user-friendliness, publisher checking, internet checking, language support etc. This evaluation of plagiarism detection software will help to enable researchers and academicians to understand the importance of plagiarism and the available functionalities of plagiarism checking tools.