Plagiarism Detection System Research Articles

Plagiarism Detection of Multi-threaded Programs Using Frequent Behavioral Pattern Mining

Software dynamic birthmark techniques construct birthmarks using the captured execution traces from running the programs, which serve as one of the most promising methods for obfuscation-resilient software plagiarism detection. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, such dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. In this paper, we propose a new dynamic thread-aware birthmark FPBirth to facilitate multi-threaded program plagiarism detection. We first explore dynamic monitoring to capture multiple execution traces with respect to system calls for each multi-threaded program under a specified input, and then leverage the Apriori algorithm to mine frequent patterns to formulate our dynamic birthmark, which can not only depict the program’s behavioral semantics, but also resist the changes and perturbations over execution traces caused by the thread scheduling in multi-threaded programs. Using FPBirth, we design a multi-threaded program plagiarism detection system. The experimental results based on a public software plagiarism sample set demonstrate that the developed system integrating our proposed birthmark FPBirth copes better with multi-threaded plagiarism detection than alternative approaches. Compared against the dynamic birthmark System Call Short Sequence Birthmark (SCSSB), FPBirth achieves 12.4%, 4.1% and 7.9% performance improvements with respect to union of resilience and credibility (URC), F-Measure and matthews correlation coefficient (MCC) metric, respectively.

Analysis of Stylometric Features and Segmentation Strategies in Intrinsic Plagiarism Detection System

Two different paradigms in the field of plagiarism detection resulting in External Plagiarism Detection (EPD) and Intrinsic Plagiarism Detection (IPD) systems. The most common applied system is EPD, which requires its algorithm to make a heuristic comparison between a suspicious document with documents in a corpus. In contrast, given a suspicious document only, an algorithm of IPD should be able to find the plagiarism section by looking for text segments having different writing styles. Previous researches for Indonesian texts fell only in the field of the EPD development system. Therefore, this research focuses on and contributes to experimenting and analyzing the stylometric features and segmentation strategies to build an IPD system for Indonesian texts. The experimentation results show that the paragraph segment performs better by scoring 0.92 for Macro Averaged-Accuracy and 0.54 for Macro Averaged-F1. The stylometric features achieving the highest scores of F-1 and Accuracy are the frequency of punctuation, the average paragraph length, and the type-token ratio.

Developments on PlagZap, the Fast and Free Textual Plagiarism Detection Solution for Universities

Abstract Plagiarism among university students is an important issue that affects their preparation and undermines the universities’ efforts to prepare skilled graduates. Universities try to fight this problem back with strict ethics policies, but they require the proper plagiarism detection tools, at affordable costs, to implement these policies. In this paper, we present PlagZap, a cost-efficient, high-volume and high-speed plagiarism detection system built using open-source software and designed to be used on textual student assignments. We discuss the advantages of this design, plagiarism obfuscation methods and empirical results from using PlagZap.

A crowdsourcing approach to construct mono-lingual plagiarism detection corpus

Plagiarism detection deals with detecting plagiarized fragments among textual documents. The availability of digital documents in online libraries makes plagiarism easier and on the other hand, to be easily detected by automatic plagiarism detection systems. Large scale plagiarism corpora with a wide variety of plagiarism cases are needed to evaluate different detection methods in different languages. Plagiarism detection corpora play an important role in evaluating and tuning plagiarism detection systems. Despite of their importance, few corpora have been developed for low resource languages. In this paper, we propose HAMTA, a Persian plagiarism detection corpus. To simulate real cases of plagiarism, manually paraphrased text are used to compile the corpus. For obtaining the manual plagiarism cases, a crowdsourcing platform is developed and crowd workers are asked to paraphrase fragments of text in order to simulate real cases of plagiarism. Moreover, artificial methods are used to scale-up the proposed corpus by automatically generating cases of text re-use. The evaluation results indicate a high correlation between the proposed corpus and the PAN state-of-the-art English plagiarism detection corpus.

Cross-language text alignment: A proposed two-level matching scheme for plagiarism detection

The exponential growth of documents in various languages throughout the web, along with the availability of several editing and translation tools have made the cross-language plagiarism detection a challenging issue. Regarding its high importance, the present study focuses on the task of cross-language text alignment also known as detailed analysis which works on the outputs of the source retrieval step of cross-language plagiarism detection systems. The paper proposes a two-level matching approach with the aim of considering both syntactic and semantic information to align plagiarism fragments from the source and suspicious documents, accurately. At the first level, a vector space model which employs a multilingual word embeddings based dictionary and a local weighting technique is used in order to extract a minimal set of highly potential candidate fragment pairs rather than considering all possible pairs of fragments. This step also contains a dynamic expansion technique to cover more candidate pairs aiming at improving the system’s recall. It is followed by a more precise algorithm that examines the candidate pairs at the sentence level using a graph-of-words representation of text. As a result, by modelling both the words and their relationships, an acceptable increase in the system’s precision which is the goal of the second level is also observed. To identify evidence of plagiarism, i.e. potential cases of unauthorized text reuse, the algorithm tries to find maximum cliques from the match graph of source and suspicious texts. With this two-level investigation, the approach is capable to discriminate true plagiarism cases from the original text. The experimental results on different datasets such as PAN-PC-11, PAN-PC-12, and SemEval-2017 show that the proposed cross-language text alignment approach significantly outperforms the state-of-the-art models and can be fed into an expert system for further improvement of cross-language plagiarism detection. The source codes are publicly available on GitHub11https://github.com/MeysamRoostaee/CLPD-DetailAnalysis/archive/master.zip, for the purposes of reproducible research.

Multi-Agents Indexing System (MAIS) for Plagiarism Detection

With the extensive availability of technological systems all over the world and the increasing diffusion of information and documents by users, especially for the Arabic population, the development of semantic plagiarism detection systems has become essential due to its importance for protecting the rights of authors. Developing such a system that can covers the content of all Arabic documents using ontology as a semantic resource would be a complex and time-consuming task and would require intelligent natural language-processing capabilities.In the context of Arabic plagiarism detection systems using an Arabic ontology and permitting these systems to support semantic representation to more efficiently verify the originality of the research and meet the needs of researchers, this paper presents a novel approach for addressing plagiarism named Multi-agents Indexing System. The proposed system is composed of three phases: (1) natural language processing phase, (2) indexing phase and (3) evaluation phase.Our experimentations are based on the training dataset released for the AraPlagDet curpus. The obtained results indicated that the proposed system has improved the performance of plagiarism detection in Arabic documents with semantic indexing and mutli-agents system.

Automated System for Source Code Plagiarism Detection

Plagiarism of code is a serious issue in today’s era. Plagiarism refers to the use of someone’s data, language and writing without proper acknowledgement of the original source. Our project basically aims to detect plagiarism in subsequent code submissions made by students. We are hosting a front-end website from where the user will be uploading the source code, after this in the website the code submissions will be made to check the extent of plagiarism in the code submissions made by students. We will check the extent of plagiarism by checking multiple parameters like number of lines of code, number of functions used, number of loops used and the extent to which keywords match in subsequent codes.

A New Hybrid Technique for Detection of Plagiarism from Text Documents

Plagiarism occurs when we use the ideas, expressions, work, and words of other authors and do not give them the required attribution. The major contributing factor in plagiarism is the availability of a high amount of data and information on the internet that can be swiftly accessed. The proposed system introduces an extrinsic plagiarism detection approach inspired by cognition because it utilizes semantic knowledge to detect the plagiarized part from the text without human involvement. A lexical database like WordNet assists the computers to perceive the data and information. These days most of the plagiarism detection systems fail to detect highly complex cases of plagiarism. The proposed system uses Dice measure as similarity measure for finding the semantic resemblance between the pair of sentences. It also uses linguistic features like path similarity, depth estimation measure to compute the resemblance between the pair of words and these features are combined by assigning different weights to them. It is capable of identifying cases like restructuring, paraphrasing, verbatim copy, and synonymized plagiarism. It has been evaluated on the PAN-PC-11 corpus. The results obtained from the proposed system signify that it has outperformed other existing systems on PAN-PC-11 in terms of precision, recall, F-measure, and PlagDet score. The proposed system has innovative approach, but the results are somehow close and reasonably better than the existing systems.

Automatic plagiarism detection in obfuscated text

Plagiarism is a serious problem in education, research, publishing and other fields. Automatic plagiarism detection systems are crucial for ensuring the integrity and genuineness of intellectual work. There are different types of plagiarism, such as copy–paste, obfuscation and translation. In particular, obfuscated text is one of the hardest types of plagiarism to detect. In this paper, we propose an automatic plagiarism detection system for obfuscated text based on a support vector machine classifier that exploits a set of lexical, syntactic and semantic features. We evaluated the performance of the proposed system on benchmark English and Arabic corpora made available by the PAN Workshop series: PAN 2012, PAN 2013, PAN 2014 and PAN@FIRE2015. We also compared the performance of our system to the performances of other systems that participated in the PAN competitions. The obtained results show that our system had the best performance in terms of the F-measure on the PAN 2012 and on the PAN@FIRE2015 obfuscated sub-corpora, was among the top four on the PAN 2013 corpus and was among the top two on the PAN 2014 corpus.

Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents

Due to the rapid increase of internet-based data, there is urgent need for a robust intelligent documents security mechanism. Although there are many attempts to build a plagiarism detection system in natural language documents, the unlimited variation and different writing styles of each character in Arabic documents make building such systems challenging. Based on its position in a word, the same Arabic letter can be written three different ways, which makes the handwritten character recognition a cumbersome process. This article proposes an intelligent unsupervised model to detect plagiarism in these documents called ASTAP. First, a handwritten Arabic character recognition system is proposed using the Grey Wolf Optimization (GWO) algorithm. Then, a modified Abstract Syntax Tree (AST) is used to match the contents of the Arabic documents to detect any similarity. Compared to the state-of-the-art methods, ASTAP improves the effectiveness of the plagiarism detection in terms of the matched similarity ratio, the precision ratio, and the processing time.

An academic Arabic corpus for plagiarism detection: design, construction and experimentation

Advancement in information technology has resulted in massive textual material that is open to appropriation. Due to researchers’ misconduct, a plethora of plagiarism detection (PD) systems have been developed. However, most PD systems on the market do not support the Arabic language. In this paper, we discuss the design and construction of an Arabic PD reference corpus that is dedicated to academic language. It consists of (2312) dissertations that were defended by postgraduate students at the University of Jordan (JU) between the years 2001–2016. This Academic Jordan University Plagiarism Detection corpus; henceforth, JUPlag, follows the Dewey decimal classification (DDC) in the way it is structured. The goal of the corpus is twofold: Firstly, it is a database for the detection of plagiarism in student assignments, reports, and dissertations. Secondly, the n-gram structure of the corpus provides a knowledgebase for linguistic analysis, language teaching, and the learning of plagiarism-free writing. The PD system is guided by JU Library’s metadata for retrieval and discovery of plagiarism. To test JUPlag, we injected an unseen dissertation with multiple instances of plagiarism-simulated paragraphs and sentences. Experimentation with the system using different verbatim n-gram segments is indeed promising. Preliminary results encourage that permission be sought to enrich this corpus with all the theses in the Thesis Repository of the Union of Arab Universities. The JUPlag corpus is intended to function as an indispensable source for testing and evaluating plagiarism detection techniques. Since the University of Jordan is seeking to become a center for plagiarism detection for Arabic content and being a non-profit organization, it will charge a nominal fee for the use of JUPlag to finance the maintenance and development of the corpus.

A New Online Plagiarism Detection System based on Deep Learning

The Plagiarism is an increasingly widespread and growing problem in the academic field. Several plagiarism techniques are used by fraudsters, ranging from a simple synonym replacement, sentence structure modification, to more complex method involving several types of transformation. Human based plagiarism detection is difficult, not accurate, and time-consuming process. In this paper we propose a plagiarism detection framework based on three deep learning models: Doc2vec, Siamese Long Short-term Memory (SLSTM) and Convolutional Neural Network (CNN). Our system uses three layers: Preprocessing Layer including word embedding, Learning Layers and Detection Layer. To evaluate our system, we carried out a study on plagiarism detection tools from the academic field and make a comparison based on a set of features. Compared to other works, our approach performs a good accuracy of 98.33 % and can detect different types of plagiarism, enables to specify another dataset and supports to compare the document from an internet search.

Scalable and language-independent embedding-based approach for plagiarism detection considering obfuscation type: no training phase

The efficiency and scalability of plagiarism detection systems have become a major challenge due to the vast amount of available textual data in several languages over the Internet. Plagiarism occurs in different levels of obfuscation, ranging from the exact copy of original materials to text summarization. Consequently, designed algorithms to detect plagiarism should be robust to the diverse languages and different types of obfuscation in plagiarism cases. In this paper, we employ text embedding vectors to compare similarity among documents to detect plagiarism. Word vectors are combined by a simple aggregation function to represent a text document. This representation comprises semantic and syntactic information of the text and leads to efficient text alignment among suspicious and original documents. By comparing representations of sentences in source and suspicious documents, pair sentences with the highest similarity are considered as the candidates or seeds of plagiarism cases. To filter and merge these seeds, a set of parameters, including Jaccard similarity and merging threshold, are tuned by two different approaches: offline tuning and online tuning. The offline method, which is used as the benchmark, regulates a unique set of parameters for all types of plagiarism by several trials on the training corpus. Experiments show improvements in performance by considering obfuscation type during threshold tuning. In this regard, our proposed online approach uses two statistical methods to filter outlier candidates automatically by their scale of obfuscation. By employing the online tuning approach, no distinct training dataset is required to train the system. We applied our proposed method on available datasets in English, Persian and Arabic languages on the text alignment task to evaluate the robustness of the proposed methods from the language perspective as well. As our experimental results confirm, our efficient approach can achieve considerable performance on the different datasets in various languages. Our online threshold tuning approach without any training datasets works as well as, or even in some cases better than, the training-base method.

An effective approach to candidate retrieval for cross-language plagiarism detection: A fusion of conceptual and keyword-based schemes

Due to the rapid growth of documents and manuscripts in various languages all over the world, plagiarism detection has become a challenging task, especially for cross lingual cases. Because of this issue, in today's plagiarism detection systems, a candidate retrieval process is developed as the first step, in order to reduce the set of documents for comparison to a reasonable number. The performance of the second step of plagiarism detection, which is devoted to a detailed analysis of the candidates is tightly dependent on the candidate retrieval phase. Regarding its high importance, the present study focuses on the candidate retrieval task and aims to extract the minimal set of highly potential source documents, accurately. The paper proposes a fusion of concept-based and keyword-based retrieval models for this purpose. A dynamic interpolation factor is used in the proposed scheme in order to combine the results of conceptual and bag-of-words models. The effectiveness of the proposed model for cross language candidate retrieval is also compared with state-of-the-art models over German-English and Spanish-English language partitions. The results show that the proposed candidate retrieval model outperforms the state-of-the-art models and can be considered as a proper choice to be embedded in cross-language plagiarism detection systems.

Academic Plagiarism Detection

This article summarizes the research on computational methods to detect academic plagiarism by systematically reviewing 239 research papers published between 2013 and 2018. To structure the presentation of the research contributions, we propose novel technically oriented typologies for plagiarism prevention and detection efforts, the forms of academic plagiarism, and computational plagiarism detection methods. We show that academic plagiarism detection is a highly active research field. Over the period we review, the field has seen major advances regarding the automated detection of strongly obfuscated and thus hard-to-identify forms of academic plagiarism. These improvements mainly originate from better semantic text analysis methods, the investigation of non-textual content features, and the application of machine learning. We identify a research gap in the lack of methodologically thorough performance evaluations of plagiarism detection systems. Concluding from our analysis, we see the integration of heterogeneous analysis methods for textual and non-textual content features using machine learning as the most promising area for future research contributions to improve the detection of academic plagiarism further.

Evaluation of State-of-the-Art Paraphrase Identification and Its Application to Automatic Plagiarism Detection

Paraphrase identification is a natural language processing (NLP) problem that involves the determination of whether two text segments have the same meaning. Various NLP applications rely on a solution to this problem, including automatic plagiarism detection, text summarization, machine translation (MT), and question answering. The methods for identifying paraphrases found in the literature fall into two main classes: similarity-based methods and classification methods. This paper presents a critical study and an evaluation of existing methods for paraphrase identification and its application to automatic plagiarism detection. It presents the classes of paraphrase phenomena, the main methods, and the sets of features used by each particular method. All the methods and features used are discussed and enumerated in a table for easy comparison. Their performances on benchmark corpora are also discussed and compared via tables. Automatic plagiarism detection is presented as an application of paraphrase identification. The performances on benchmark corpora of existing plagiarism detection systems able to detect paraphrases are compared and discussed. The main outcome of this study is the identification of word overlap, structural representations, and MT measures as feature subsets that lead to the best performance results for support vector machines in both paraphrase identification and plagiarism detection on corpora. The performance results achieved by deep learning techniques highlight that these techniques are the most promising research direction in this field.

Towards Building an Arabic Plagiarism Detection System: Plagiarism Detection in Arabic

This article describes a plagiarism detection system for the Arabic language that combines different similarity-measure techniques to uncover plagiarism in Arabic documents. The proposed system consists of two main components, one document-retrieval and the other detailed similarity analysis. The document-retrieval component generates queries from a given suspicious document and makes use of Google search API to retrieve candidate source documents from the Web. The similarity analysis component takes each source document in turn and attempts to identify the plagiarized parts in the suspicious document. The proposed system is thoroughly evaluated using an indigenous corpus. At the document-retrieval level, the system achieved above 75% accuracy in terms of f-score, whereas at the detailed similarity-computation level, the f-score is above 70%.

Searching source code fragments using incremental clustering

SummaryPlagiarism is becoming an increasingly serious problem in academic environment. In this paper, we deal with a specific kind of plagiarism: source code plagiarism. In this case, there is no software available for detecting plagiarism on a larger scale (hundreds of student submissions every year). We propose algorithms for source code parsing and processing as a part of a complex system for plagiarism detection. A source code vectorization using characteristic vectors is a vital piece of the whole process, and k‐means algorithm helps with the classification and clustering of vectors. Student assignments are submitted regularly, and any plagiarism detection system needs to handle them as they come. For this reason, we propose a modified incremental k‐means algorithm and a method for determining the number of clusters. We also consider methods for vector search among clusters and suggest the use of conditional entropy to select the important vector elements used in the search algorithm. Our results show how the proposed algorithms and methods work on real student submissions.

Analysis of student’s final qualification theses using text loans detection systems

In this paper there are results of the bachelor and master theses citing analysis. These students graduated from the Higher mathematics chair of the Russian Technological University in the summer of 2018. In this comparative analysis the dependencies of thesis loan percent on parameters of students, statistical values of their theses are explored. This research is actual because of the progress and development of new informational technologies used in the educational system. Popularity of the text loan detection systems increases. Automatic plagiarism detection systems are intended to make educational process better, the text drawing search easier, to support the copyright laws and academical honesty. The percentage is given by two main Russian plagiarism detection systems: Antiplagiat and Rucontext. Connections between thesis parameters are explored. Advantages of each text loan detection systems are described. In this research there are the results of the pedagogical experiment aimed to analyze statistically the dependencies of the bachelor’s and master’s theses loan percentage which have been got from Antiplagiat and Rucontext systems on the author’s parameters, statistical values describing thesis text. The comparison between statistical results of these systems have been made. The conclusions about their advantages have been presented in the paper. In order to make the comparison methods of the mathematical statistics have been used. Numerical experiment has been provided by means of the packages of the R statistical language. The difference between text loan percentages in the Antiplagiat and Rucontext systems has been analyzed. It has been shown that it grows when length of the text becomes larger. The dependencies of the text loan percentage on the available parameters of the thesis author and text parameters have been presented. The dependencies types are the same for the both systems. Scale of the coefficients in the statistical dependencies is also the same. The difference is in the very set of the parameters: the Rucontext percentage is better described statistically with the sex of the author, the Antiplagiat percentage is described with the type of the higher education (bachelor’s or master’s thesis). Also the dependency of the text loan percentage on the length of the thesis text differs: the Antiplagiat percentage is better described statistically with the number of words but the Rucontext percentage is described with the number of characters. It seems that these differences can be explained with different text search and analyze algorithms. The dependencies between the Rucontext percentage and the Antiplagiat text loan percentage is presented.

Students’ views on plagiarism and plagiarism detection systems

Plagiaat on ülikoolides endiselt suur probleem. Tehnoloogia areng on võimaldanud üliõpilastele lihtsa juurdepääsu veebimaterjalidele, see aga mõjutab nende pla gieerimiskäitumist, muutes selle levinumaks kui kunagi varem. Uurimuse eesmärk on välja selgitada üliõpilaste arusaamad plagiaadist ja plagiaadituvastussüsteemide kasutamisest. Selleks analüüsiti 380-lt eri valdkonna ja õppetaseme üliõpilaselt küsimustikuga kogutud andmeid, millest ilmneb, et üle poole vastajatest ei pea plagiaati levinud probleemiks ega tea täpselt, milles plagiaat seisneb. Sotsiaalteaduste ning humanitaarteaduste ja kunstide valdkonna üliõpilased on võrreldes loodus- ja täppisteaduste ning meditsiinivaldkonna üliõpilastega plagiaadi olemusest ja plagiaadituvastussüsteemidest teadlikumad. Plagiaadi leviku pidurdamiseks peetakse oluliseks ennetustööd: akadeemiliste tekstide kirjutamise oskuse ja õpioskuste arendamist, plagiaadi olemuse kohta ühtsete arusaamade kujundamist ja plagiaadijuhtumite menetlemiseks institutsioonisiseste eeskirjade kehtestamist. Plagiaadituvastussüsteemide kasutamine on üliõpilaste hinnangul õiglane, aga ka hirmu teki tav. Sellised tulemused osutavad vajadusele suurendada õppejõudude vastutust teema käsitlemisel ainekursuste raames. Summary