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Distinguishing Abstracts of Human-Written and ChatGPT-Generated Papers in the Field of Computer Science

Authors :

Mohsen Arzani¹ Hamed Vahdat-Nejad² Matin Hossein-Pour³

1- Perlab, Faculty of Electrical and Computer Engineering, University of Birjand 2- Perlab, Faculty of Electrical and Computer Engineering, University of Birjand 3- Perlab, Faculty of Electrical and Computer Engineering, University of Birjand

Keywords :

LLM-generated text،Deepfake text،Deep learning،ChatGPT

Abstract :

In the era of artificial intelligence, advanced technologies such as text deep fakes have emerged, utilizing AI and deep learning algorithms to generate text contents that convincingly mimic reality. Text-based deep fakes, commonly found in emails, articles, news, and social media posts, pose a significant threat to public trust. Our research proposes a method to distinguish between fake and genuine scientific abstracts in the field of computer science using a specialized dataset and deep learning models. The proposed detection model can differentiate between real and fake abstracts with 95% accuracy. Additionally, we employed metrics such as precision, recall, accuracy, and F1 score to measure the performance of our system in detail.

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