An Integrated Machine Learning Approach for Extrinsic Plagiarism Detection

Alsallal, Muna; Iqbal, Rahat; Amin, Saad; James, Anne; Palade, Vasile

doi:10.1109/dese.2016.1

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…The problem of plagiarism detection is closely related to authorship attribution. AlSallal et al [9] used common words and content words with SVM to detect plagiarism. Shahid et al [42] used syntactic and lexical features with SVM to detect "spun" content.…”

Section: Machine Learning Methods For Plagiarism Detectionmentioning

confidence: 99%

A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X

Mahmood

Ahmad

Shafiq

et al. 2019

Proceedings on Privacy Enhancing Technologies

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Authorship attribution aims to identify the author of a text based on the stylometric analysis. Authorship obfuscation, on the other hand, aims to protect against authorship attribution by modifying a text's style. In this paper, we evaluate the stealthiness of state-of-the-art authorship obfuscation methods under an adversarial threat model. An obfuscator is stealthy to the extent an adversary finds it challenging to detect whether or not a text modified by the obfuscator is obfuscated -a decision that is key to the adversary interested in authorship attribution. We show that the existing authorship obfuscation methods are not stealthy as their obfuscated texts can be identified with an average F1 score of 0.87. The reason for the lack of stealthiness is that these obfuscators degrade text smoothness, as ascertained by neural language models, in a detectable manner. Our results highlight the need to develop stealthy authorship obfuscation methods that can better protect the identity of an author seeking anonymity.

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Section: Machine Learning Methods For Plagiarism Detectionmentioning

confidence: 99%

A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X

Mahmood

Ahmad

Shafiq

et al. 2019

Proceedings on Privacy Enhancing Technologies

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“…Plagiarism in the literal sense is often done through exact copy of text in whole or in part. Near copy is done from various sources with a little alteration like insertion, deletion, substitution, spliting or joining sentences, Modified copy is done through changing the syntax or reordering phrases in the original text [6].…”

Section: Kinds Of Plagiarismmentioning

confidence: 99%

Analytical Study of Traditional and Intelligent Textual Plagiarism Detection Approaches

Ali¹,

Taqa²

2022

J.Edu.Sci.

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The Web provides various kinds of data and applications that are readily available to explore and are considered a powerful tool for humans. Copyright violation in web documents occurs when there is an unauthorized copy of the information or text from the original document on the web; this violation is known as Plagiarism. Plagiarism Detection (PD)can be defined as the procedure that finds similarities between a document and other documents based on lexical, semantic, and syntactic textual features. The approaches for numeric representation (vectorization) of text like Vector Space Model (VSM) and word embedding along with text similarity measures such as cosine and jaccard are very necessary for plagiarism detection. This paper deals with the concepts of plagiarism, kinds of plagiarism, textual features, text similarity measures, and plagiarism detection methods, which are based on intelligent or traditional techniques. Furthermore, different types of traditional and algorithms of deep learning for instance, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are discussed as a plagiarism detector. Besides that, this work reviews many other papers that give attention to the topic of Plagiarism and its detection.

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“…This method can find the relevance between words and words, sentences and sentences, and paragraphs and paragraphs, so as to detect the similarity of papers at the semantic level, thereby improving the accuracy of detection. AlSallal et al [14] proposed a new weighting method and used Latent Semantic Analysis (LSA) as the style feature for internal plagiarism detection. Resnik et al [15] used the WordNet model to calculate semantic similarity.…”

Section: Style Breach Detectionmentioning

confidence: 99%

AMDnet: An Academic Misconduct Detection Method for Authors’ Behaviors

Zhou¹,

Xu²,

Han³

et al. 2022

Computers, Materials &Amp; Continua

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In recent years, academic misconduct has been frequently exposed by the media, with serious impacts on the academic community. Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript. For the most part, these techniques can only detect cases of textual plagiarism, which means that potential culprits can easily avoid discovery through clever editing and alterations of text content. In this paper, we propose an academic misconduct detection method based on scholars' submission behaviors. The model can effectively capture the atypical behavioral approach and operation of the author. As such, it is able to detect various types of misconduct, thereby improving the accuracy of detection when combined with a text content analysis. The model learns by forming a dual network group that processes text features and user behavior features to detect potential academic misconduct. First, the effect of scholars' behavioral features on the model are considered and analyzed. Second, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address the problem of imbalanced samples of positive and negative classes among contributing scholars. Finally, the text features of the papers are combined with the scholars' behavioral data to improve recognition precision. Experimental results on the imbalanced dataset demonstrate that our model has a highly satisfactory performance in terms of accuracy and recall.

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An Integrated Machine Learning Approach for Extrinsic Plagiarism Detection

Cited by 12 publications

References 18 publications

A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X

A Girl Has No Name: Automated Authorship Obfuscation using Mutant-X

Analytical Study of Traditional and Intelligent Textual Plagiarism Detection Approaches

AMDnet: An Academic Misconduct Detection Method for Authors’ Behaviors

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