{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:55:49Z","timestamp":1774454149804,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kunlun Talent Project"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The development of generative artificial intelligence technology has brought convenience to various industries, but also caused some confusion. Especially today, when the content generated by large language models is extremely similar to real text, it has created challenges in many fields (such as in the discrimination of graduation theses in schools) in quickly identifying whether a text is from human sources or generated by large language models. Based on the DeepSeek-R1 language model, this paper combines natural language features and uses a judgment mechanism to detect text generated by large language models. Experimental results show that its accuracy is improved compared with conventional methods in the Reuters, WP and HC3 datasets.<\/jats:p>","DOI":"10.3390\/info17040320","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:05:33Z","timestamp":1774451133000},"page":"320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection of LLM-Generated Text vs. Human Text via DeepSeek-R1 Multi-Feature Fusion"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6331-9076","authenticated-orcid":false,"given":"Xuan","family":"Bu","sequence":"first","affiliation":[{"name":"School of Intelligent Science and Engineering, Qinghai Minzu University, Xining 810007, China"},{"name":"School of Cyberspace Security, Qinghai Minzu University, Xining 810007, China"},{"name":"Joint Laboratory of Cyberspace Security, Qinghai Minzu University, Xining 810007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minghu","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Engineering, Qinghai Minzu University, Xining 810007, China"},{"name":"School of Cyberspace Security, Qinghai Minzu University, Xining 810007, China"},{"name":"Joint Laboratory of Cyberspace Security, Qinghai Minzu University, Xining 810007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Engineering, Qinghai Minzu University, Xining 810007, China"},{"name":"School of Cyberspace Security, Qinghai Minzu University, Xining 810007, China"},{"name":"Joint Laboratory of Cyberspace Security, Qinghai Minzu University, Xining 810007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5764-9293","authenticated-orcid":false,"given":"Jiayi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Engineering, Qinghai Minzu University, Xining 810007, China"},{"name":"School of Cyberspace Security, Qinghai Minzu University, Xining 810007, China"},{"name":"Joint Laboratory of Cyberspace Security, Qinghai Minzu University, Xining 810007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Qinghai Normal University, Xining 810007, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3390\/bdcc9030050","article-title":"On Continually Tracing Origins of LLM-Generated Text and Its Application in Detecting Cheating in Student Coursework","volume":"9","author":"Wang","year":"2025","journal-title":"Big Data Cogn. Comput."},{"key":"ref_2","unstructured":"(2025). DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1002\/pra2.1079","article-title":"Recognizing Large-Scale AIGC on Search Engine Websites Based on Knowledge Integration and Feature Pyramid Network","volume":"61","author":"Wang","year":"2024","journal-title":"Proc. Assoc. Inf. Sci. Technol."},{"key":"ref_4","first-page":"62","article-title":"Taking ChatGPT as an Example to Explore the New Challenges of Network Security in the AIGC Era","volume":"2","author":"Ma","year":"2025","journal-title":"Ind. Inf. Secur."},{"key":"ref_5","unstructured":"Bao, G., Zhao, Y., Teng, Z., Yang, L., and Zhang, Y. (2024, January 7\u201311). Fast-DetectGPT: Efficient zero-shot detection of machine-generated text via conditional probability curvature. Proceedings of the International Conference on Learning Representations, Vienna, Austria."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1145\/3624725","article-title":"The science of detecting LLM-generated text","volume":"67","author":"Tang","year":"2024","journal-title":"Commun. ACM"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2330002","DOI":"10.1142\/S2717554523300025","article-title":"AI-Generated Text Detection: Challenges and Future Directions","volume":"33","author":"An","year":"2023","journal-title":"Int. J. Asian Lang. Process."},{"key":"ref_8","unstructured":"Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., Radford, A., Krueger, G., Kim, J.W., and Kreps, S. (2019). Release Strategies and the Social Impacts of Language Models. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gehrmann, S., Strobelt, H., and Rush, A.M. (2019). GLTR: Statistical Detection and Visualization of Generated Text. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Association for Computational Linguistics.","DOI":"10.18653\/v1\/P19-3019"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ippolito, D., Duckworth, D., Callison-Burch, C., and Eck, D. (2020). Automatic Detection of Generated Text is Easiest when Humans are Fooled. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Association for Computational Linguistics, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.acl-main.164"},{"key":"ref_11","unstructured":"Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., and Choi, Y. (2019). Defending Against Neural Fake News. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Curran Associates Inc."},{"key":"ref_12","first-page":"154","article-title":"Large language model (llm) ai text generation detection based on transformer deep learning algorithm","volume":"14","author":"Mo","year":"2024","journal-title":"Int. J. Eng. Manag. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.aej.2025.03.139","article-title":"Automated detection of ChatGPT-generated text vs. human text using gannet-optimized deep learning","volume":"124","author":"Alshareef","year":"2025","journal-title":"Alex. Eng. J."},{"key":"ref_14","first-page":"21","article-title":"Research on the Detection of Elements in Al Generation and Scholar Writing Papers","volume":"4","author":"Xiong","year":"2024","journal-title":"Artif. Intell. Sci. Eng."},{"key":"ref_15","unstructured":"Mitchell, E., Lee, Y., Khazatsky, A., Manning, C.D., and Finn, C. (2023). DetectGPT: Zero-shot machine-generated text detection using probability curvature. Proceedings of the 40th International Conference on Machine Learning, PMLR."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chakraborty, U., Gheewala, J., Deegadwala, S., Vyas, D., and Soni, M. (2024). Safeguarding authenticity in text with BERT-powered detection of AI-generated content. Proceedings of the International Conference on Inventive Computation Technologies, IEEE.","DOI":"10.1109\/ICICT60155.2024.10544590"},{"key":"ref_17","unstructured":"Mao, C., Vondrick, C., Wang, H., and Yang, J. (2024). Raidar: geneRative AI Detection viA Rewriting. Proceedings of the International Conference on Learning Representations, OpenReview."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"129829","DOI":"10.1016\/j.neucom.2025.129829","article-title":"Zero-shot detection of LLM-generated text via text reorder","volume":"63","author":"Sun","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, P., Qiu, X., and Huang, X. (2017). Adversarial Multi-task Learning for Text Classification. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL), Association for Computational Linguistics.","DOI":"10.18653\/v1\/P17-1001"},{"key":"ref_20","first-page":"1098","article-title":"Large Language Model-Generated Text Detection Based on Linguistic Feature Ensemble Learning","volume":"24","author":"Xiang","year":"2024","journal-title":"Netinfo Secur."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Macko, D., Moro, R., Uchendu, A., Lucas, J., Yamashita, M., Pikuliak, M., Srba, I., Le, T., Lee, D., and Simko, J. (2023). Multitude: Large-scale multilingual machine-generated text detection benchmark. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2023.emnlp-main.616"},{"key":"ref_22","unstructured":"Krishna, K., Song, Y., Karpinska, M., Wieting, J., and Iyyer, M. (2023, January 10\u201316). Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense. Proceedings of the Conference on Neural Information Processing Systems, New Orleans, LA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bhattacharjee, A., Kumarage, T., Moraffah, R., and Liu, H. (2023). Contrastive domain adaptation for AI-generated text detection. Proceedings of the International Joint Conference on Natural Language Processing and the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2023.ijcnlp-main.40"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_25","unstructured":"Hansen, L.K. (2022). Higher-Order Statistics in Machine Learning, MIT Press."},{"key":"ref_26","first-page":"967","article-title":"mL-BFGS: A Momentum-based L-BFGS for Distributed Large-scale Neural Network Optimization","volume":"2023","author":"Niu","year":"2023","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, X., Shen, X., Chen, Z., Backes, M., and Zhang, Y. (2024). MGTBench: Benchmarking Machine-Generated Text Detection. Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, Association for Computing Machinery.","DOI":"10.1145\/3658644.3670344"},{"key":"ref_28","unstructured":"Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., and Wu, Y. (2023). How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection. arXiv."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/4\/320\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:10:57Z","timestamp":1774451457000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/4\/320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,25]]},"references-count":28,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["info17040320"],"URL":"https:\/\/doi.org\/10.3390\/info17040320","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,25]]}}}