Identifying academic plagiarism is a pressing problem, among others, for research institutions, publishers, and funding organizations. Detection approaches proposed so far analyze lexical, syntactical, and semantic text similarity. These approaches find copied, moderately reworded, and literally translated text. However, reliably detecting disguised plagiarism, such as strong paraphrases, sense-for-sense translations, and the reuse of non-textual content and ideas, is an open research problem. The thesis addresses this problem by proposing plagiarism detection approaches that implement a different concept: analyzing non-textual content in academic documents, specifically citations, images, and mathematical content. To validate the effectiveness of the proposed detection approaches, the thesis presents five evaluations that use real cases of academic plagiarism and exploratory searches for unknown cases. The evaluation results show that non-textual content elements contain a high degree of semantic information, are language-independent, and largely immutable to the alterations that authors typically perform to conceal plagiarism. Analyzing non-textual content complements text-based detection approaches and increases the detection effectiveness, particularly for disguised forms of academic plagiarism. To demonstrate the benefit of combining non-textual and text-based detection methods, the thesis describes the first plagiarism detection system that integrates the analysis of citation-based, image-based, math-based, and text-based document similarity. The system's user interface employs visualizations that significantly reduce the effort and time users must invest in examining content similarity.

中文翻译：

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分析非文本内容元素以检测学术剽窃

识别学术剽窃是研究机构、出版商和资助组织等面临的紧迫问题。迄今为止提出的检测方法分析词汇、句法和语义文本相似性。这些方法可以找到复制的、适度改写的和字面翻译的文本。然而，可靠地检测伪装的抄袭，例如强释义、意义翻译以及非文本内容和想法的重用，是一个开放的研究问题。该论文通过提出实现不同概念的抄袭检测方法来解决这个问题：分析学术文件中的非文本内容，特别是引文、图像和数学内容。为了验证所提出的检测方法的有效性，论文提出了五项评估，利用学术抄袭的真实案例和对未知案例的探索性搜索。评估结果表明，非文本内容元素包含高度的语义信息，与语言无关，并且在很大程度上无法改变作者通常为掩盖抄袭而进行的更改。分析非文本内容补充了基于文本的检测方法并提高了检测效率，特别是对于伪装形式的学术剽窃。为了证明结合非文本和基于文本的检测方法的好处，本文描述了第一个剽窃检测系统，该系统集成了基于引用、基于图像、基于数学和基于文本的文档相似性分析。系统'