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Figure plagiarism and manipulation, an under-recognised problem in academia
Thomas Saliba1, David Rotzinger2
1Radiology Department, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland. Tes1066@hotmail.com.
European Radiology
|February 12, 2025
Summary
Artificial intelligence (AI) presents new plagiarism challenges in academic research, especially with image manipulation. Enhanced vigilance and AI detection tools are crucial for maintaining research integrity, particularly in fields like medical imaging.
Area of Science:
- Scientific integrity
- Artificial intelligence in research
- Academic publishing
Background:
- Academic plagiarism threatens scientific integrity.
- Artificial intelligence (AI) introduces new challenges in text and image generation/manipulation.
- Existing plagiarism detection tools struggle with AI-generated and manipulated content.
Purpose of the Study:
- To discuss the evolving landscape of plagiarism with AI proliferation.
- To focus on implications for text and image manipulation in academic writing.
- To highlight the need for enhanced detection methods for AI-generated and manipulated content.
Main Methods:
- Review of current tools and practices for detecting AI-generated/manipulated content.
- Discussion of plagiarism detection software, computer vision algorithms, and reverse image searches.
- Analysis of AI's dual role in enhancing readability and facilitating plagiarism.
Main Results:
- Text-based AI detection is adapting, but image-based detection lags.
- AI manipulation detection tools show promise but are imperfect, especially for images.
- Reverse image searches can identify some manipulated figures, but human oversight is vital.
Conclusions:
- Integrating image fabrication, manipulation, and plagiarism detection into fraud detection is essential.
- Enhanced vigilance and technology are critical for upholding academic integrity.
- Fields like medical imaging require robust detection due to direct impact on clinical outcomes.
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