How can science uphold integrity in an era where large language models (LLMs) can seamlessly disguise stolen ideas? The renowned journal Spektrum der Wissenschaft explores this question in its recent article “Das Katz-und-Maus-Spiel um die Redlichkeit” (“Cat-and-Mouse Game with Integrity”).

The piece features our latest research on tackling the evolving challenges of plagiarism detection in a digital world with unlimited access to AI tools. It highlights an interview with Dr. André Greiner-Petter, who shares insights from his research stay in Japan, where he is developing an artificial dataset of LLM-generated plagiarism. This pioneering work aims to uncover hidden risks and strengthen existing detection algorithms.

Read the full article on Spektrum.de (note: access requires a subscription): Plagiate, KI und Redlichkeit – Die Wissenschaft im digitalen Dilemma

More Information

• HyPlag – Hybrid Plagiarism Detection
• Mathematical Information Retrieval (MathIR)

Related Publications

 J. Bevendorff, D. Dementieva, M. Froebe, B. Gipp, A. Greiner-Petter, J. Karlgren, M. Mayerl, P. Nakov, A. Panchenko, M. Potthast, A. Shelmanov, E. Stamatatos, B. Stein, Y. Wang, M. Wiegmann, and E. Zangerle, “Overview of PAN 2025: Generative AI Detection, Multilingual Text Detoxification, Multi-Author Writing Style Analysis, and Generative Plagiarism Detection,” in Proceedings of the 47th European Conference on Information Retrieval (ECIR), 2025.

  A. Satpute, N. Giessing, A. Greiner-Petter, M. Schubotz, O. Teschke, A. Aizawa, and B. Gipp, “Can LLMs Master Math? Investigating Large Language Models on Math Stack Exchange,” in Proceedings of 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24), Washington, USA, 2024.

  N. Meuschke, Analyzing Non-Textual Content Elements to Detect Academic Plagiarism, Springer Fachmedien Wiesbaden, 2023. Video of Doctoral Defense.

  J. P. Wahle, T. Ruas, F. Kirstein, and B. Gipp, “How Large Language Models are Transforming Machine-Paraphrased Plagiarism,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022, pp. 952-963.

  T. Foltynek, N. Meuschke, and B. Gipp, “Academic Plagiarism Detection: A Systematic Literature Review,” ACM Computing Surveys, vol. 52, iss. 6, p. 112:1–112:42, 2019.

  B. Gipp, Citation-based Plagiarism Detection – Detecting Disguised and Cross-language Plagiarism using Citation Pattern Analysis, Springer Vieweg Research, 2014.

We are delighted to announce that Dr. Norman Meuschke was awarded this year’s Airbus Research Award ‘Claude Dornier’ for his outstanding research achievements during his Ph.D. research. The award ceremony took place on July 14th, 2022, at the University of Konstanz.

Norman defended his Ph.D. thesis titled “Analyzing Non-Textual Content Elements to Detect Academic Plagiarism” summa cum laude on March 5th, 2021.

The objective of his Ph.D. research was to research and develop new approaches capable of detecting instances of plagiarism in academic literature, even in cases where the plagiarism has been carefully disguised, e.g., through paraphrasing, or through ‘only’ stealing ideas, mathematical formulae, or images, instead of verbatim text. Norman achieved this by developing and combining novel document similarity assessment methods, which compute not only the similarity between text but also the similarity among academic citations, mathematical formulae, as well as images, figures, and charts.
In his current research, Norman continues to build on the approaches and solutions he presented in his thesis. Together with other researchers, he has plans to release a production-ready system that can be freely used by academics and researchers to detect instances of strongly disguised academic plagiarism.

Video of the award ceremony:

Related links:

• On this page, you can read more about Norman’s Ph.D. research and download his thesis.
• This news article summarizes other highlights of the award ceremony not included in the video.