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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM)
Marwan Omar
Dept. of Computer Science, Capitol Technology University, Maryland, USA
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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TextGuard: Identifying and Neutralizing Adversarial Threats in Textual Data Luay Albtsoh; Marwan Omar
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 2 (2025): INJIISCOM: VOLUME 6, ISSUE 2, DECEMBER 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i2.15232

Abstract

Adversarial attacks in the text domain pose a serious risk to the integrity of Natural Language Processing (NLP) systems. In this study, we propose "TextGuard," a unique approach to detect hostile instances in NLP, based on the Local Outlier Factor (LOF) algorithm. This paper compares TextGuard's performance against traditional NLP classifiers like LSTM, CNN, and transformer-based models, experimentally verifying its effectiveness across various real-world datasets. To contextualize our findings and demonstrate its superiority, TextGuard significantly surpasses earlier state-of-the-art methods like DISP and FGWS, achieving F1 recognition accuracy scores up to 94.8%. Consequently, this sets a new benchmark as the first application of the LOF technique for adversarial example identification within the text domain.
Co-Authors Luay Albtsoh