Advertisement

Figure Plagiarism Detection Using Content-Based Features

  • Taiseer EisaEmail author
  • Naomie Salim
  • Salha Alzahrani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 555)

Abstract

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.

Keywords

Figure plagiarism detection Content feature Similarity detection 

Notes

Acknowledgements

This work is supported by the Malaysian Ministry of Higher Education and the Research Management Centre at the Universiti Teknologi Malaysia under Research University Grant Category Vot: Q.J130000.2528.13H46.

References

  1. 1.
    Arrish, S., Afif, F. N., Maidorawa, A., & Salim, N. (2014). Shape-Based Plagiarism Detection for Flowchart Figures in Texts. International Journal of Computer Science & Information Technology (IJCSIT) 6(1), 113–124, doi: 10.5121/ijcsit.2014.6108.
  2. 2.
    Bhatia, S., & Mitra, P. (2012). Summarizing figures, tables, and algorithms in scientific publications to augment search results. ACM Trans. Inf. Syst., 30(1), 1–24, doi: 10.1145/2094072.2094075.
  3. 3.
    Futrelle, R. P. Handling figures in document summarization. In Proc. of the ACL-04 Workshop: Text Summarization Branches Out, Barcelona, Spain, 25–26 July 2004 (pp. 61–65).Google Scholar
  4. 4.
    Hiremath, S., & Otari, M. (2014). Plagiarism Detection-Different Methods and Their Analysis: Review. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 1(7), 41–47.Google Scholar
  5. 5.
    Lee, P.-s., West, J. D., & Howe, B. (2016). Viziometrics: Analyzing visual information in the scientific literature. arXiv preprint arXiv:1605.04951.
  6. 6.
    Ovhal, P. M., & Phulpagar, B. D. (2015). Plagiarized Image Detection System based on CBIR. International Journal of Emerging Trends & Technology in Computer Science, 4(3).Google Scholar
  7. 7.
    Potthast, M., Stein, B., Barr, A., #243, n-Cede, #241, et al. (2010). An evaluation framework for plagiarism detection. Paper presented at the Proceedings of the 23rd International Conference on Computational Linguistics: Posters, Beijing, China.Google Scholar
  8. 8.
    Rabiu, I., & Salim, N. (2014). Textual and Structural Approaches to Detecting Figure Plagiarism in Scientific Publications. Journal of Theoretical and Applied Information Technology, 70(2), 356–371.Google Scholar
  9. 9.
    Roig, M. (2006). Avoiding plagiarism, self-plagiarism, and other questionable writing practices: a guide to ethical writing.Google Scholar
  10. 10.
    Zhang, X.-x., Huo, Z.-l., & Zhang, Y.-h. (2014). Detecting and (Not) Dealing with Plagiarism in an Engineering Paper: Beyond CrossCheck—A Case Study. [journal article]. Science and Engineering Ethics, 20(2), 433–443, doi: 10.1007/s11948-013-9460-5.
  11. 11.
    Zhang, Y.-h. H., Jia, X.-y., Lin, H.-f., & Tan, X.-f. (2013). Be careful! Avoiding duplication: a case study. Journal of Zhejiang University. Science. B, 14(4), 355.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Faculty of ComputingUniversiti Teknologi MalaysiaSkudaiMalaysia
  2. 2.Department of Computer ScienceTaif UniversityTaifSaudi Arabia

Personalised recommendations