Jul 2026· International journal of advances in soft computing and its applications· Vol 18, pp. 376-384· 0 citations· 24 references
TL;DR
A novel model, Detecting Embedded Plagiarized Text in Images (DEPTI), is introduced to identify plagiarized text embedded within images, demonstrating high accuracy and robust performance.
Abstract
This paper evaluates plagiarism detection using deep learning and natural language processing (NLP) techniques. A novel model, Detecting Embedded Plagiarized Text in Images (DEPTI), is introduced to identify plagiarized text embedded within images, demonstrating high accuracy and robust performance. DEPTI effectively recognizes paraphrased, translated, and artificial intelligence generated content, achieving strong detection capabilities across diverse scenarios. The model integrates PAN-PC-11, TF-IDF, Tesseract OCR, DistilBERT, and LSTM to extract and analyze text from images, enabling advanced plagiarism detection beyond conventional approaches. Experimental results confirm DEPTI’s effectiveness, highlighting its potential as a reliable tool for safeguarding academic integrity in the digital era.
This study introduces the Artificial Intelligence Scientific Text Detector, a hybrid model combining BigBird embeddings with a CNN-BiLSTM framework that can effectively identify LLM-generated content, supporting the integrity and credibility of academic publications.
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.
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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.
Yang Yu, Wang Gao· International Journal of Sci...· 0 citations
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.
Xue-Cong Li, Xiao-Hong Li, Qiang Hu et al.· Proceedings of the Thirty-Fi...· 0 citations
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.
Marwah Alian, Dana Halabi, H. Alshboul· Bulletin of Electrical Engin...· 0 citations
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