AI Content Detection

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).