Plagiarism is to steal others’ work using their words directly or indirectly without a credit citation. Copying others’ ideas is another type of plagiarism that may occur in many areas but the most serious one is the academic plagiarism. Academic misconduct forms high-profile plagiarism cases at universities. Therefore, technical solutions are strictly demanded for automatic idea plagiarism detection. Detection of figure plagiarism is a challenge field of research because not only the text analytics but also graphic features are analyzed. This paper investigates the issue of idea and figure plagiarism and proposes a detection method which copes with text and structure change. The procedure depends on finding similar semantic meanings between figures by applying image processing and semantic mapping techniques.

Content-Based Scientific Figure Plagiarism Detection Using Semantic Mapping

Flowchart Plagiarism Detection System: An Image Processing Approach

Plagiarism is considered as a serious offence. Due to the tremendious growth of internet resources someone's idea can be used without giving them the proper credit. There are certain plagiarism detection tools available, for example: Plagiarisma.net, Viper etc. A flaw has been identified in this system that when a document is getting scanned for plagiarism each and every line is tested if any image is detected during scan then that image is simply discarded, which is very inappropriate because an image can also be plagiarized. Architecture diagram, flow diagram, UML diagrams, even snapshots of test results can be plagiarized Hence there is need to develop a system which can detect plagiarism in images too.

Detecting plagiarism in images

Plagiarism is the process of copying someone else’s text or figure verbatim or without due recognition of the source. A lot of techniques have been proposed for detecting plagiarism in texts, but a few techniques exist for detecting figure plagiarism. This paper focuses on detecting plagiarism in scientific figures. Existing techniques are not applicable to figures. Detecting plagiarism in figures requires extraction of information from its components to enable comparison between figures. Consequently, content-based figure plagiarism detection technique is proposed and evaluated based on the existing limitations. The proposed technique was based on the feature extraction and similarity computation methods. Feature extraction method is capable of extracting contextual features of figures in aid of understanding the components contained in figures, while similarity detection method is capable of categorizing a figure either as plagiarized or as non-plagiarized depending on the threshold value. Empirical results showed that the proposed technique was accurate and scalable.

Figure Plagiarism Detection Using Content-Based Features

In an academic environment, plagiarism is the process of copying someone else's text, idea or data verbatim or without due recognition of the source, which is a serious academic offence. Many techniques have been proposed in the literature for detecting plagiarism in texts, but only a few techniques exist for detecting figure plagiarism. The main problem associated with existing techniques of plagiarism detection is that they are not applicable to non-textual elements of figures in research publications. This paper focuses on detecting plagiarism in scientific figures. Textual-reference representation based figure plagiarism detection techniques are proposed and evaluated, based on existing limitations. The proposed techniques use enhanced feature extraction such as textual features and similarity computation methods such as similarity based on textual-reference of figures. The enhanced feature extraction method was found to be capable of extracting textual references such as captions and description texts. The similarity detection method was capable of categorising a given figure as either plagiarised or non-plagiarised from a source collection of scientific publications, depending on a certain threshold value. Results showed that the proposed technique achieved precision=0.78 and recall=0.67 result in terms of the evaluation measure.

Figure plagiarism detection based on textual features representation

Plagiarism detection is well known phenomenon in the academic arena. Copying other people is considered as serious offence that needs to be checked. There are many plagiarism detection systems such as turn-it-in that has been developed to provide this checks. Most, if not all, discard the figures and charts before checking for plagiarism. Discarding the figures and charts results in look holes that people can take advantage. That means people can plagiarized figures and charts easily without the current plagiarism systems detecting it. There are very few papers which talks about flowcharts plagiarism detection. Therefore, there is a need to develop a system that will detect plagiarism in figures and charts. This paper presents a method for detecting flow chart figure plagiarism based on shape-based image processing and multimedia retrieval. The method managed to retrieve flowcharts with ranked similarity according to different matching sets.

Fadhil Noer Afif

Papers

Shape-Based Plagiarism Detection for Flowchart Figures in Texts

Shape-Based Plagiarism Detection for Flowchart Figures in Texts