An Adaptive Image-Based Plagiarism Detection Approach. Meuschke, N., Gondek, C., Seebacher, D., Breitinger, C., Keim, D., & Gipp, B. In Proceedings of the 18th ACM/IEEE Joint Conference on Digital Libraries (JCDL), pages 131–140, Fort Worth, USA, June, 2018. Venue Rating: CORE A*![An Adaptive Image-Based Plagiarism Detection Approach [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
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Code doi abstract bibtex 1 download Identifying plagiarized content is a crucial task for educational and research institutions, funding agencies, and academic publishers. Plagiarism detection systems available for productive use reliably identify copied text, or near-copies of text, but oſten fail to detect disguised forms of academic plagiarism, such as paraphrases, trans- lations, and idea plagiarism. To improve the detection capabilities for disguised forms of academic plagiarism, we analyze the images in academic documents as text-independent features. We propose an adaptive, scalable, and extensible image-based plagiarism de- tection approach suitable for analyzing a wide range of image similarities that we observed in academic documents. The proposed detection approach integrates established image analysis methods, such as perceptual hashing, with newly developed similarity assess- ments for images, such as ratio hashing and position-aware OCR text matching. We evaluate our approach using 15 image pairs that are representative of the spectrum of image similarity we observed in alleged and confirmed cases of academic plagiarism. We embed the test cases in a collection of 4,500 related images from academic texts. Our detection approach achieved a recall of 0.73 and a pre- cision of 1. These results indicate that our image-based approach can complement other content-based feature analysis approaches to retrieve potential source documents for suspiciously similar con- tent from large collections. We provide our code as open source to facilitate future research on image-based plagiarism detection.
@inproceedings{MeuschkeGSB18,
address = {Fort Worth, USA},
title = {An {Adaptive} {Image}-{Based} {Plagiarism} {Detection} {Approach}},
isbn = {978-1-4503-5178-2},
url = {paper=https://gipp.com/wp-content/papercite-data/pdf/meuschke2018.pdf code=https://purl.org/imagepd},
doi = {10.1145/3197026.3197042},
abstract = {Identifying plagiarized content is a crucial task for educational and research institutions, funding agencies, and academic publishers. Plagiarism detection systems available for productive use reliably identify copied text, or near-copies of text, but oſten fail to detect disguised forms of academic plagiarism, such as paraphrases, trans- lations, and idea plagiarism. To improve the detection capabilities for disguised forms of academic plagiarism, we analyze the images in academic documents as text-independent features. We propose an adaptive, scalable, and extensible image-based plagiarism de- tection approach suitable for analyzing a wide range of image similarities that we observed in academic documents. The proposed detection approach integrates established image analysis methods, such as perceptual hashing, with newly developed similarity assess- ments for images, such as ratio hashing and position-aware OCR text matching. We evaluate our approach using 15 image pairs that are representative of the spectrum of image similarity we observed in alleged and confirmed cases of academic plagiarism. We embed the test cases in a collection of 4,500 related images from academic texts. Our detection approach achieved a recall of 0.73 and a pre- cision of 1. These results indicate that our image-based approach can complement other content-based feature analysis approaches to retrieve potential source documents for suspiciously similar con- tent from large collections. We provide our code as open source to facilitate future research on image-based plagiarism detection.},
booktitle = {Proceedings of the 18th {ACM}/{IEEE} {Joint} {Conference} on {Digital} {Libraries} ({JCDL})},
author = {Meuschke, Norman and Gondek, Christopher and Seebacher, Daniel and Breitinger, Corinna and Keim, Daniel and Gipp, Bela},
month = jun,
year = {2018},
note = {Venue Rating: CORE A*},
keywords = {Plagiarism Detection},
pages = {131--140},
}
Downloads: 1
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