Abstract
The integrity and reliability of scientific literature is facing a serious threat by adversarial text generation techniques, specifically from the use of automated paraphrasing tools to mask plagiarism. These tools generate "tortured phrases", statistically improbable synonyms (e.g. "counterfeit consciousness" for "artificial intelligence"), that preserve the local grammar while obscuring the original source. Most existing detection methods depend heavily on static blocklists or general-domain language models, which suffer from high false-negative rates for novel obfuscations and cannot determine the source of the plagiarized content. In this paper, we propose Semantic Reconstruction of Adversarial Plagiarism (SRAP), a framework designed not only to detect these anomalies but to mathematically recover the original terminology. We use a two-stage architecture: (1) statistical anomaly detection with a domain-specific masked language model (SciBERT) using token-level pseudo-perplexity, and (2) source-based semantic reconstruction using dense vector retrieval (FAISS) and sentence-level alignment (SBERT). Experiments on a parallel corpus of adversarial scientific text show that while zero-shot baselines fail completely (0.00 percent restoration accuracy), our retrieval-augmented approach achieves 23.67 percent restoration accuracy, significantly outperforming baseline methods. We also show that static decision boundaries are necessary for robust detection in jargon-heavy scientific text, since dynamic thresholding fails under high variance. SRAP enables forensic analysis by linking obfuscated expressions back to their most probable source documents.
Abstract (translated)
科学文献的完整性和可靠性正面临着敌对文本生成技术的严重威胁，尤其是自动改写工具被用于掩盖抄袭行为。这些工具会产生“扭曲的短语”，即统计上不太可能的同义词（例如，“伪造意识”替代“人工智能”），它们在保留局部语法的同时隐藏了原始来源。现有的大多数检测方法依赖于静态黑名单或通用领域的语言模型，这导致对新颖混淆的假阴性率高，并且无法确定抄袭内容的来源。在这篇论文中，我们提出了语义重构敌对剽窃（SRAP）框架，该框架不仅设计用于检测这些异常情况，还旨在通过数学手段恢复原始术语。 我们的方法采用两阶段架构：(1) 使用领域特定的掩码语言模型（SciBERT）进行统计异常检测，并利用标记级伪困惑度；(2) 使用密集向量检索（FAISS）和句子级别对齐（SBERT）进行基于源语义重构。在对抗性科学文本平行语料库上的实验表明，零样本基准方法完全失败（恢复准确率为0.00%），而我们的增强检索方法实现了23.67%的恢复准确性，显著优于基线方法。 此外，我们还展示了静态决策边界对于识别富含专业术语的科学文献中的剽窃行为是必需的，因为动态阈值在高方差情况下无法正常工作。SRAP通过将混淆表达链接回最可能的源文档来实现法医分析。
URL
https://arxiv.org/abs/2512.10435
