FEATURES OF DETECTING ACADEMIC IMAGE PLAGIARISM

Authors

  • Yuliia Hlavcheva
  • Maksym Glavchev

DOI:

https://doi.org/10.26906/SUNZ.2025.3.055

Keywords:

academic integrity, academic image plagiarism, visual content, image manipulation, academic documents

Abstract

The rapid growth of visual content in academic documents highlights the issue of detecting academic image plagiarism. Unlike textual plagiarism, which is effectively identified by modern systems, visual plagiarism remains a challen ging task due to the diversity of its manifestations and the potential for manipulation. The aim of this work is to analyze existing approaches to image recognition and comparison for detecting academic plagiarism and to identify key features. The study examines computer vision algorithms, deep learning models, and other technologies for analyzing textual elements within images. A classification of visual plagiarism forms is proposed, including exact copying, affine transformations, semantic duplication, and image generation. The main features are identified: extensive possibilities for manipulation and specific forms of visual content representation, lack of a global image database, difficulty in extracting non-textual content, and legal restrictions on access to full texts. Conclusions: Image analysis for detecting academic plagiarism remains a relevant and promising research direction, which can be implemented through the following avenues: improving modification detection algorithms and integrating visual analysis into textual plagiarism detection systems, creating image repositories, and ensuring the availability of informational tools for experts and researchers.

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Published

2025-09-30

Issue

Vol. 3 No. 81 (2025): Control, Navigation and Communication Systems

Section

Information Technology

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Copyright (c) 2025 Yuliia Hlavcheva, Maksym Glavchev

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.