基于文本的抄袭检测技术的比较分析
Muhammad Sajid1, Muhammad Sanaullah1, Muhammad Fuzail2
1Department of Computer Science, Air University, Islamabad, Pakistan.
PloS one
|April 8, 2025
概括
由于人工智能内容生成,盗版检测至关重要. 这项研究审查了计算方法,发现结合文本和非文本分析提供了最有希望的准确检测方法.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 数字法医学数字法医学
背景情况:
- 大型语言模型和可访问的在线信息的兴起增加了窃案例.
- 学生在教育中越来越多地使用技术,这加剧了检测复制内容的挑战.
- 对于复杂而准确的抄袭检测系统的需求日益增长.
研究的目的:
- 系统地审查和分类用于窃检测 (PD) 的计算方法.
- 评估各种检测技术在区分人类生成和人工智能生成内容方面的有效性.
- 为理解抄袭预防和检测研究提出一个结构化的框架.
主要方法:
- 在2019年至2024年期间发表的189篇研究论文的系统文献综述.
- 对不同抄袭检测技术的比较分析.
- 对区分人类作者文本与人工智能生成文本的方法的评估.
主要成果:
- 抄袭检测领域正在积极研究,在识别复杂抄袭方面取得了重大进展.
- 关键的进步包括对非文本内容的探索,机器学习应用程序和增强的语义文本分析.
- 目前的研究表明,在自动检测高度模糊和难以识别的抄袭案例方面取得了进展.
结论:
- 结合文本和非文本内容特征的多种分析方法是未来研究最有前途的方向.
- 需要进一步的研究来完善检测越来越复杂的抄袭形式的检测技术.
- 多种分析方法的整合将提高抄袭检测系统的准确性和稳定性.
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