Plagiarism Detection Algorithm Research Articles

Detection of Source Code Plagiarism Utilizing an Approach Based on Machine Learning

Academic institutions, which often publish papers and journals, are ideal testing grounds for the efficacy of counterfeit detection methods. Plagiarism occurs when someone uses the words of another writer without giving that writer proper credit. The proliferation of freeware text editors and the increasing availability of scientific materials online have made the detection of plagiarism a pressing concern; however, the detection of plagiarism in the source code presents a particularly difficult problem. Plagiarism detection algorithms for identification systems and software source code have been the subject of numerous academic investigations. The proposed method combines TF-IDF transformations with K-means clustering to achieve a 99.2% accuracy rate when detecting instances of plagiarism in the source code. This is because it groups similar lines of code together. On the other hand, in comparison to the outcomes produced by the random forest algorithm, the ones that it generates are significantly better. The performance of the MOSS system that was already in place was inferior to that of the system that was used for 90% and 80% of the training set. When contrasting the results, some parameters for evaluation that are considered include precision, recall, and F-measure. The proposed system is implemented in Jupyter Notebook 7 and Python. Also, graphic user interface is designed and implemented to give user friendly experience to the users.

Building A Web-Based Final Project Detection Information System with Incremental Method and Jaccard Similarity Algorithm

Thesis is a scientific work and one of the graduation requirements for students in tertiary institutions. The process of implementing the thesis at the Information Technology Study Program at the University of Timor is still done manually, starting from submitting titles to data processing which allows for duplicate titles, difficulty finding data and losing data. For this reason, a system is needed to avoid the risk of duplicate titles and difficulty finding the data. So in this study an information system will be built to detect the similarity of the title of the final project (Thesis) using the incremental method and the Jaccard similarity algorithm as a plagiarism detection algorithm. The application of the incremental method is due to its flexible nature and less risk of failure than other development methods. System testing is carried out on the functional system and using the User Acceptance Test. Based on the test results, this system facilitates Study Programs in submitting final assignment titles, finding final assignment data, minimizing the risk of duplicate titles through the Title Similarity Percentage testing feature to find out the highest level of similarity based on thesis title category and avoiding data loss.

Important Arguments Nomination Based on Fuzzy Labeling for Recognizing Plagiarized Semantic Text

Plagiarism is an act of intellectual high treason that damages the whole scholarly endeavor. Many attempts have been undertaken in recent years to identify text document plagiarism. The effectiveness of researchers’ suggested strategies to identify plagiarized sections needs to be enhanced, particularly when semantic analysis is involved. The Internet’s easy access to and copying of text content is one factor contributing to the growth of plagiarism. The present paper relates generally to text plagiarism detection. It relates more particularly to a method and system for semantic text plagiarism detection based on conceptual matching using semantic role labeling and a fuzzy inference system. We provide an important arguments nomination technique based on the fuzzy labeling method for identifying plagiarized semantic text. The suggested method matches text by assigning a value to each phrase within a sentence semantically. Semantic role labeling has several benefits for constructing semantic arguments for each phrase. The approach proposes nominating for each argument produced by the fuzzy logic to choose key arguments. It has been determined that not all textual arguments affect text plagiarism. The proposed fuzzy labeling method can only choose the most significant arguments, and the results were utilized to calculate similarity. According to the results, the suggested technique outperforms other current plagiarism detection algorithms in terms of recall, precision, and F-measure with the PAN-PC and CS11 human datasets.

An external plagiarism detection system based on part-of-speech (POS) tag n-grams and word embedding

The aim of this paper is to present an automatic plagiarism detection system to identify plagiarized passages of documents. Our plagiarism detection system uses both syntactic and semantic similarities to identify plagiarized passages. Our proposed method is a novel contribution because of its usage of part-of-speech tag n-grams (POSNG) which are able to show syntactic similarities between source and suspicious sentences. Each source document is indexed according to part-of-speech (POS) tag n-grams by a search engine in order to access rapidly to sentences that are possible plagiarism candidates. Even though our plagiarism detection system obtains very good results just using POS tag n-grams, its performance is further improved with the usage of semantic similarities. The semantic relatedness between words is measured with the word embedding technique called Word2Vec and the longest common subsequence approach is used to measure the semantic similarity between source and suspicious sentences. There are several types of plagiarism such as verbatim, paraphrasing, source-code, and cross-lingual. The high obfuscation paraphrasing is a type of plagiarism and its detection is one of the most difficult plagiarism detection tasks. Our proposed method, which is based on POS tag n-grams, improves the detection performance of the high obfuscation paraphrasing type and is the main contribution of this paper. For this study, we use the large dataset called PAN-PC-11 which is created for the evaluation of automatic plagiarism detection algorithms. Our experiments are conducted with the four types of paraphrasing in PAN-PC-11 which are none, low, high and simulated obfuscation paraphrasing types. We defined various threshold and parameter settings in order to assess the diversity of our results. We compared the performance of our method with the plagiarism detectors in the 3rd International Competition on Plagiarism Detection (PAN11). According to the experimental results, the proposed method achieved the best performance in terms of plagdet measure in the types of high and low obfuscation paraphrasing and produced competitive results in the other paraphrasing types.

Evaluation of Fingerprint Selection Algorithms for Two-Stage Plagiarism Detection

Abstract Generally, the process of plagiarism detection can be divided into two main stages: source retrieval and text alignment. The paper evaluates and compares effectiveness of five fingerprint selection algorithms used during the source retrieval stage: Every p-th, 0 mod p, Winnowing, Frequency-biased Winnowing (FBW) and Modified FBW (MFBW). The algorithms are evaluated on a dataset containing plagiarism cases in Bachelor and Master Theses written in English in the field of computer science. The best performance is reached by 0 mod p, Winnowing and MFBW. For these algorithms, reduction of fingerprint size from 100 % to about 20 % kept the effectiveness at approximately the same level. Moreover, MFBW sends overall fewer document pairs to the text alignment stage, thus also reducing the computational cost of the process. The software developed for this study is freely available at the author’s website http://www.cs.rtu.lv/jekabsons/.

ANALYSIS WINNOWING ALGORITHM FOR TEXT PLAGIARISM DETECTION USING THREE METHOD SIMILARITY

Cases of plagiarism in recent years has been an issues. Based on that issues, this research will create a system to detect similarity in a text. There is an aspect as reference of the research that is analyze the plagiarism algorithm. This research will analyze the accuracy one of plagiarism check algorithm, winnowing algorithm. Winnowing algorithm is a plagiarism detection algorithm based on document fingerprinting. To calculate percentage similarity of document fingerprinting in text, there are 3 methods to measure similarity that will be used in this research, which is jaccard similarity coefficient, sorensen dice similarity coefficient, and berg similarity coefficient.

Taxonomy of academic plagiarism methods

The article gives an overview of the plagiarism domain, with focus on academic plagiarism. The article defines plagiarism, explains the origin of the term, as well as plagiarism related terms. It identifies the extent of the plagiarism domain and then focuses on the plagiarism subdomain of text documents, for which it gives an overview of current classifications and taxonomies and then proposes a more comprehensive classification according to several criteria: their origin and purpose, technical implementation, consequence, complexity of detection and according to the number of linguistic sources. The article suggests the new classification of academic plagiarism, describes sorts and methods of plagiarism, types and categories, approaches and phases of plagiarism detection, the classification of methods and algorithms for plagiarism detection. The title of the article explicitly targets the academic community, but it is sufficiently general and interdisciplinary, so it can be useful for many other professionals like software developers, linguists and librarians.

Plagiarism Detection Algorithm Model Based on NLP Technology

We can bear in mind that each of us has plagiarized a text without realizing that it was plagiarism, Plagiarism can happen in Articles, Papers, Researches, literature, music, software, scientific, newspapers, websites, Master and PHD Thesis and many other fields, So plagiarism has become serious major problem to teachers, researchers and publishers, There are divergent opinions about how to define plagiarism and what makes plagiarism serious. So, the detecting plagiarism is very important, so in this survey we explicate the concept of ;plagiarism ; and provide an overview of different plagiarism software and tools to solve the plagiarism problem, and will discuss the plagiarism process, types and detection methodologies. We can define that plagiarism is the brief and the description of this sentence ;someone used someone else’s mental product (such as its texts, ideas, or privacy). We suggest that what makes plagiarism so reprehensible is that it distorts scientific credit. In addition, intentional plagiarism indicates dishonesty. Moreover, there are a number of possible negative consequences of plagiarism. So we just create a framework for external plagiarism detection in which a some NLP processes are applied to process a set of suspicious and original documents, we have classified the different plagiarism detection techniques based on Lexical, Semantic, Syntactic and grammar analysis algorithms, And all of these algorithms precedes it NLP processing.

Plagiarism Detection Algorithm Model Based on NLP Technology

Plagiarism Detection Algorithm Model Based on NLP Technology

Web Scraping and Winnowing Algorithms for Plagiarism Detection of Final Project Titles

Plagiarism in research can occur due to accident or intentional. Plagiarism is an act that violates copyright and includes actions that harm others. In submitting the title of the research, for example, for the final assignment research, not a few students who repeatedly submitted titles were rejected and considered doing plagiarism because the title proposed had already existed before. Then we need a system that can detect the similarity between the titles to be submitted and the existing titles so that it is expected to reduce the occurrence of plagiarism. This study uses a winnowing algorithm to find the percentage similarity between titles. The Google Scholar will be used to obtain data on research titles that have been previously available as comparison titles. Web scraping with CURL (Client URLs) and simple HTML DOM parser is used to retrieve title data from Google Scholar. The results of the study with the application of a Winnowing algorithm to find the percentage similarity to data from Google Scholar were able to present a percentage of similarities in percent with the category of mild, moderate or severe plagiarism, while also helping early detection as prevention of plagiarism.

MODERN ALGORITHMS TO IDENTIFY PLAGIARISM

Nowadays information technology industry is growing extremely fast. To solve business needs, address researcher demand for problem solving a lot of programs are built from scratch. However, not all developers are fair enough to align their products with the corresponding library or another (open source) product licenses, i.e. copyrights are being violated, intentionally or due to familiarization with another source code. To address this issue in past decade multiple plagiarism detection techniques and algorithms were invented. Despite the fact, that many of them are capable of code comparison on meta-level, modern Integrated Development Environments (IDEs) provide convenient way to modify program source code without actual re-writing, preserving the original code workflow and avoiding plagiarism detection. This paper will compare and identify available approaches to apprehend this issue, as well as provide insights for the future this problem mitigation.

Integración de plataforma LMS y algoritmo de código abierto para detección y prevención de plagio en Educación Superior

The work shows the results of the first phase of a research project, aimed at addressing from a new perspective the problem of plagiarism in the digital era and as part of the processes of academic training, which is pursued through the integration of technological tools . It is an application for plagiarism detection built from two open source applications, a platform for distance education and an adaptive plagiarism detection algorithm. The methodology for the development, integration and implementation of the aforementioned tool is also presented in general terms, as well as the analysis of statistical data from a sample of students at the higher level of the Faculty of Information Sciences (FCI) of the Autonomous University of San Luis Potosí (UASLP), Mexico. The initial analysis yields data of interest and probabilistic models as a first approach to the problem, which will be analyzed from an academic focus, in such a way as to identify the degree of participation of students in this type of practice as part of their university education.

Plagiarism Detection Techniques for Arabic Script Languages: A Literature Review

Plagiarism is generally defined as literary theft and academic dishonesty. This considered as the serious issue in an academic documents and texts. There are numerous of plagiarism detection techniques have been developed for various natural languages, mainly English. In this paper we investigate and review the plagiarism detection techniques and algorithms which have been developed for Arabic Script Languages (ASL), and providing a literature review of the utilized methods in terms of techniques and outcomes. The result of this paper will help the researchers who are going to commence their development and extend their researches in ASL like Arabic, Persian, Urdu, and Kurdish.

Docode 5: Building a real-world plagiarism detection system

Plagiarism refers to the appropriation of someone else’s ideas and expression. Its ubiquity makes it necessary to counter it, and invites the development of commercial systems to do so. In this document we introduce Docode 5, a system for plagiarism detection that can perform analyses on the World Wide Web and on user-defined collections, and can be used as a decision support system. Our contribution in this document is to present this system in all its range of components, from the algorithms used in it to the user interfaces, and the issues with deployment on a commercial scale at an algorithmic and architectural level. We ran performance tests on the plagiarism detection algorithm showing an acceptable performance from an academic and commercial point of view, and load tests on the deployed system, showing that we can benefit from a distributed deployment. With this, we conclude we can adapt algorithms made for small-scale plagiarism detection to a commercial-scale system.

A Plagiarism Detection Algorithm based on Extended Winnowing

Plagiarism is a common problem faced by academia and education. Mature commercial plagiarism detection system has the advantages of comprehensive and high accuracy, but the expensive detection costs make it unsuitable for real-time, lightweight application environment such as the student assignments plagiarism detection. This paper introduces the method of extending classic Winnowing plagiarism detection algorithm, expands the algorithm in functionality. The extended algorithm can retain the text location and length information in original document while extracting the fingerprints of a document, so that the locating and marking for plagiarism text fragment are much easier to achieve. The experimental results and several years of running practice show that the expansion of the algorithm has little effect on its performance, normal hardware configuration of PC will be able to meet small and medium-sized applications requirements. Based on the characteristics of lightweight, high efficiency, reliability and flexibility of Winnowing, the extended algorithm further enhances the adaptability and extends the application areas.

IChecker

Academic plagiarism is regarded as a serious offense and much effort in the past has been devoted to build stand-alone plagiarism detection systems for a specific language. This paper proposes a new information retrieval-based plagiarism detection algorithm that handles multilingual documents and enables seamless integration with learning management systems. The proposed algorithm employs information retrieval and sequence matching techniques to identify suspected plagiarized sentences and permits parametric control to reduce both false-positive and false-negative results. The full-featured implementation, called iChecker, not only could quickly identify suspected plagiarized works but also ease academics' effort to evaluate the severity of the offence by a quantified measure. Currently iChecker is adopted by over 300 courses (with some having several hundred of students) and has obtained satisfactory results. During 2012 to 2016, iChecker has processed and verified a total of 276,943 documents in English, Traditional Chinese and Simplified Chinese text.

Exploiting thread-related system calls for plagiarism detection of multithreaded programs

Dynamic birthmarking used to be an effective approach to detecting software plagiarism. Yet the new trend towards multithreaded programming renders existing algorithms almost useless, due to the fact that thread scheduling nondeterminism severely perturbs birthmark generation and comparison. In this paper, we redesign birthmark based software plagiarism detection algorithms to make such approach effective for multithreaded programs. Our birthmarks are abstractions of program behavioral characteristics based on thread-related system calls. Such birthmarks are less susceptible to thread scheduling as the system calls are the sources that impose thread scheduling rather than being affected. We have conducted an empirical study on a benchmark that consists of 234 versions of 35 different multithreaded programs. Our experiments show that the new birthmarks are superior to existing birthmarks and are resilient against most state-of-the-art obfuscation techniques.

Software Plagiarism Detection with Birthmarks Based on Dynamic Key Instruction Sequences

A software birthmark is a unique characteristic of a program. Thus, comparing the birthmarks between the plaintiff and defendant programs provides an effective approach for software plagiarism detection. However, software birthmark generation faces two main challenges: the absence of source code and various code obfuscation techniques that attempt to hide the characteristics of a program. In this paper, we propose a new type of software birthmark called DYnamic Key Instruction Sequence (DYKIS) that can be extracted from an executable without the need for source code. The plagiarism detection algorithm based on our new birthmarks is resilient to both weak obfuscation techniques such as compiler optimizations and strong obfuscation techniques implemented in tools such as SandMark , Allatori and Upx . We have developed a tool called DYKIS-PD (DYKIS Plagiarism Detection tool) and conducted extensive experiments on large number of binary programs. The tool, the benchmarks and the experimental results are all publicly available.

Boosting Algorithm and Meta-Heuristic Based on Genetic Algorithms for Textual Plagiarism Detection

Day after day, the plagiarism cases increase and become a crucial problem in the modern world, caused by the quantity of textual information available in the web and the development of communication means such as email service. This paper deals on the unveiling of two plagiarism detection systems: Firstly boosting system based on machine learning algorithm (decision tree C4.5 and K nearest neighbour) composed on three steps (text pre-processing, first detection, and second detection). Secondly using genetic algorithm based on an initial population generated from the dataset used a fitness function fixed and the reproduction rules (selection, crossover, and mutation). For their experimentation, the authors have used the benchmark pan 09 and a set of validation measures (precision, recall, f-measure, FNR, FPR, and entropy) with a variation in configuration of each system; They have compared their results with the performance of other approaches found in literature; Finally, the visualisation service was developed that provides a graphical vision of the results using two methods (3D cub and a cobweb) with the possibility to have a detailed and global view using the functionality of zooming and rotation. The authors' aims are to improve the quality of plagiarism detection systems and preservation of copyright.

Plagiarism Detection Algorithm for Source Code in Computer Science Education

Nowadays, computer programming is getting more necessary in the course of program design in college education. However, the trick of plagiarizing plus a little modification exists among some students' home works. It's not easy for teachers to judge if there's plagiarizing in source code or not. Traditional detection algorithms cannot fit this condition. The author designed an effective and complete method to detect source code plagiarizing according to the popular way of students' plagiarizing. There are two basic concepts of the algorithm. One is to standardize the source code via filtration against to remove the majority noises intentionally blended by plagiarists. The other one is an improved Longest Common Subsequence algorithm for text matching, using statement as the unit for matching. The authors also designed an appropriate HASH function to increase the efficiency of matching. Based on the algorithm, a system was designed and proved to be practical and sufficient, which runs well and meet the practical requirement in application.