Detecting Plagiarized Text in Images using OCR and NLP-Based Deep Learning Approaches

LLM-generated scientific content detection method

Arabic Plagiarism Detection Using Word2Vec-Based Semantic Features and Random Forest Classification on the ExAraPlagDet Dataset

The findings underscore the potential of advanced NLP techniques to overcome language-specific challenges, providing a foundation for future research in multilingual plagiarism detection and enhancing the development of tools for other languages facing similar challenges.

Combining probabilistic features and semantic features for AI-Generated text detection

The proliferation of Large Language Models (LLMs) such as ChatGPT and Gemini has resulted in a surge of AI-generated text across various domains. However, the widespread use of this technology raises concerns regarding the generation of misinformation and malicious content. To address this challenge, we propose a novel AI-generated Text Detection model combining Probabilistic and Semantic features (ATDPS). Our model extracts semantic features using a pre-trained language model and combines them with probabilistic features generated by multiple LLMs. A temporal convolutional network is employed to process sequence probabilistic features, effectively capturing temporal characteristics within the text. To ensure data coherence and diversity, our dataset includes text generated by a variety of LLMs, including the latest models like GPT-4. Experimental results demonstrate ATDPS's superior performance over existing baselines in terms of accuracy, precision, recall and F1 score, highlighting its potential and effectiveness in detecting AI-generated text.

VaryBalance: Detecting LLM-generated Text through Variation

The core of VaryBalance is that, compared to LLM-generated texts, there is a greater difference between human texts and their rewritten version via LLMs, and quantifies this through Mean Squared Deviation and distinguishes human texts and LLM-generated texts.

International Journal of Informatics, Information System and Computer Engineering

Evaluating lexical feature extraction for plagiarism detection in Arabic documents

This study introduces an external plagiarism detection framework built on an artificial neural network model and a lexical feature extraction framework adapted to the linguistic features of Arabic, verifying its effectiveness for Arabic plagiarism detection.