Skip to main content
SpringerLink
Log in

AI Content Detection

  • Conference paper
  • First Online:
Advanced Computing (IACC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2053))

Included in the following conference series:

  • 122 Accesses

Abstract

The rise of AI-generated data, mainly from models like ChatGPT, LLAMA2 poses serious difficulties to academic integrity and raises worries about plagiarism. The current research looks on the competences of various AI content recognition algorithms to distinguish between human and AI-authored material. This research looks at numerous research papers, publication years, datasets, machine learning approaches, and the benefits and drawbacks of detection methods in AI text detection. Various datasets and machine learning techniques are employed, with various types of classifier emerging as a top performer. This work creates an Extra tree classifier that can distinguish ChatGPT produced text from human authored content. “ChatGPT Paraphrase” dataset was used for model training and testing. The result shows that the proposed model resulted in 80.1% accuracy and outperformed the existing models namely Linear Regression (LR), Support Vector Machine (SVM), Decision Tree, (DT), K-Nearest Neighbour (KNN), Ada Boost Classifier (ABC), Random Forest Classifier (RFC), Bagging Classifier (BG), Gradient Boosting Classifier (GBC).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gritsay, G., Grabovoy, A., Chekhovich, Y.: Automatic detection of machine generated texts: need more tokens. In: 2022 Ivannikov Memorial Workshop (IVMEM), Moscow, Russian Federation, pp. 20–26 (2022). https://doi.org/10.1109/IVMEM57067.2022.9983964

  2. Elali, F.R., Rachid, L.N.: AI-generated research paper fabrication and plagiarism in the scientific community. CellPress https://doi.org/10.1016/j.patter.2023.100706

  3. Uzun, L.: ChatGPT and academic integrity concerns: detecting artificial intelligence generated content. Technology (LET Journal) 3, 45–54 (2023)

    Google Scholar 

  4. Khalil, M., Er, E.: Will ChatGPT get you caught? Rethinking of plagiarism detection. In: Zaphiris, P., Ioannou, A. (eds.) Learning and Collaboration Technologies. HCII 2023. LNCS, vol. 14040, pp. 475–487. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-34411-4_32

  5. Elkhatat, A.M., Elsaid, K., Almeer, S.: Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. Int. J. Educ. Integr. https://doi.org/10.1007/s40979-023-00140-5

  6. Ma, Y., et al.: AI vs. Human -- Differentiation Analysis of Scientific Content Generation. arXiv, arXiv:2301.10416 [cs.CL]

  7. Islam, N., Sutradhar, D., Noor, H., Raya, J.T., Maisha, M.T., Farid, D.M.: Distinguishing Human Generated Text from ChatGPT Generated Text Using Machine Learning. arXiv, arXiv:2306.01761 [cs.CL]

  8. Alamleh, H., AlQahtani, A.A.S., ElSaid, A.: Distinguishing human-written and ChatGPT-generated text using machine learning. In: 2023 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, USA, pp. 154–158 (2023). https://doi.org/10.1109/SIEDS58326.2023.10137767

  9. Corizzo, R., Leal-Arenas, S.: One-class learning for AI-generated essay detection. Appl. Sci. (Switzerland) 13(13). https://doi.org/10.3390/app13137901

  10. Weber-Wulff, D., et al.: Testing of Detection Tools for AI-Generated Text. arXiv. arXiv:2306.15666 [cs.CL]

  11. Katib, I., Assiri, F.Y., Abdushkour, H.A., Hamed, D., Ragab, M.: Differentiating chat generative pretrained transformer from humans: detecting ChatGPT-generated text and human text using machine learning. MDPI, Mathematics (2023). https://doi.org/10.3390/math11153400

  12. Mitrovic, S., Andreoletti, D., Ayoub, O.: ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-Generated Text. arXiv. arXiv:2301.13852 [cs.CL]

  13. Harada, A., Bollegala, D., Chandrasiri, N.P.: Discrimination of human-written and human and machine written sentences using text consistency. In: 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, pp. 41–47 (2021). https://doi.org/10.1109/ICCCIS51004.2021.9397237

  14. Mitchell, E., Lee, Y., Khazatsky, A., Manning, C.D., Finn, C.: DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature. arXiv, arXiv:2301.11305 [cs.CL]

Download references

Author information

Authors and Affiliations

  1. G H Raisoni College of Engineering and Management, Pune, India

    Rachna Sable, Vaishali Baviskar, Devang Pagare, Eshan Kasliwal, Devashri Bhosale & Pratik Jade

  2. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India

    Sudhanshu Gupta

Authors
  1. Rachna Sable
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vaishali Baviskar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sudhanshu Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Devang Pagare
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eshan Kasliwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Devashri Bhosale
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pratik Jade
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rachna Sable .

Editor information

Editors and Affiliations

  1. SR University, Warangal, India

    Deepak Garg

  2. COPELABS, Lusófona University, Lisbon, Portugal

    Joel J. P. C. Rodrigues

  3. Bennett University, Greater Noida, India

    Suneet Kumar Gupta

  4. Swansea University, Wales, UK

    Xiaochun Cheng

  5. Lovely Professional University, Phagwara, India

    Pushpender Sarao

  6. SITCOE Engineering College, Ichalkaranji, India

    Govind Singh Patel

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sable, R. et al. (2024). AI Content Detection. In: Garg, D., Rodrigues, J.J.P.C., Gupta, S.K., Cheng, X., Sarao, P., Patel, G.S. (eds) Advanced Computing. IACC 2023. Communications in Computer and Information Science, vol 2053. Springer, Cham. https://doi.org/10.1007/978-3-031-56700-1_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-56700-1_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56699-8

  • Online ISBN: 978-3-031-56700-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation