Adaptive AI-Based Plagiarism Detection System

References

[1] Aiken A. MOSS: A system for detecting software plagiarism. Stanford University. 1994.

[2] Pennington J, Socher R, Manning CD. GloVe: Global vectors for word representation. In: Proc. 2014 Conf. Empirical Methods Natural Lang. Process. (EMNLP). 2014. p. 1532–1543.

[3] Reimers N, Gurevych I. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. In: Proc. 2019 Conf. Empirical Methods Natural Lang. Process. (EMNLP). 2019. p. 3982–3992.

[4] Saadatpanah P, Shafahi A, Goldstein T. Adversarial attacks on copyright detection systems. In: Proc. 2nd Workshop Adversarial Machine Learning in Real-World Computer Vision Applications. 2019. p. 1–8.

[5] Qamar, Ali Z, Malik S, Khan AQ, Ahmar M. AI-based plagiarism detection system. International Journal of Innovations in Science & Technology. 2025;7(10):184–189.

[6] Shaikh F, Siddavatam S. AI-based plagiarism detection system. International Journal of Engineering Research & Technology (IJERT). 2026;14(2):1–5.

[7] Salton G, Buckley C. Term-weighting approaches in automatic text retrieval. Information Processing & Management. 1988;24(5):513–523.

[8] Cavnar WB, Trenkle JM. N-gram-based text categorization. In: Proc. 3rd Annual Symposium on Document Analysis and Information Retrieval (SDAIR). 1994. p. 161–169.

[9] Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proc. NAACL-HLT. 2019. p. 4171–4186.

[10] Alzahrani MA, Palade V, Salim N. On retrieving intelligently plagiarized documents using semantic similarity. Engineering Applications of Artificial Intelligence. 2015;45:246–258.

@article{1723909,
      author = {Ayush Kumar, Deepak Kumar V, Saurav Das, Deepti N N},
      title = {Adaptive AI-Based Plagiarism Detection System},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {1715-1720},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723909.pdf},
      abstract = {Traditional plagiarism detection systems normally compute a separate similarity score applying a corrected combination of detection techniques, regardless of the type of plagiarism presence. This approach performs poorly when the nature of text-based overlap varies; for instance, verbatim copying produces high scores across all word-related measures, whereas rephrasing produces low word-related similarity but high meaning-related similarity. This paper presents an adaptive plagiarism detection system that first computes four unaffiliated similarity measures; exact sequence correspondence, TF-IDF cosine similarity, n-gram overlap, and Sentence-BERT meaning-related similarity; then classifies the anticipated plagiarism pattern utilizing a customizable, rule-based classifier, and ultimately re-weights the four measures according to the detected pattern before computing a last and ultimate adaptive score. The system moreover offers sentence-level evidence and a traceable, non-templated explanation.},
      keywords = {plagiarism detection; natural language processing; adaptive weighting; semantic similarity; Sentence-BERT; TF-IDF; explainable AI},
      month = {October},
  }