Leveraging Pre-Trained Language Models for Realistic Adversarial Attacks

Nuzaer Omar; Ademola Adesokan; Sanjay Madria

doi:10.1109/BigData66926.2025.11401655

Leveraging Pre-Trained Language Models for Realistic Adversarial Attacks

Nuzaer Omar
, Ademola Adesokan
, Sanjay Madria

Computer Science

Missouri University of Science and Technology
University of Central Arkansas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Original language	English
Title of host publication	Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025
Editors	Cheng-Zhong Xu, Leong Hou U, Xueqi Cheng, Jing Gao, Giuseppe Polese, Hong Mei, Paul Boniol, Michiaki Tatsubori, Chen Zhao, Dawei Zhou, Xiaohua Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1514-1521
Number of pages	8
Edition	2025
ISBN (Electronic)	9798331594473
DOIs	https://doi.org/10.1109/BigData66926.2025.11401655
State	Published - 2025
Event	2025 IEEE International Conference on Big Data, BigData 2025 - Macau, China Duration: Dec 8 2025 → Dec 11 2025

Conference

Conference	2025 IEEE International Conference on Big Data, BigData 2025
Country/Territory	China
City	Macau
Period	12/8/25 → 12/11/25

ASJC Scopus Subject Areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

Keywords

Adversarial Attack
Black-box Attack
Masked Language Models
Phonetic Perturbation
Robustness Evaluation

Access to Document

10.1109/BigData66926.2025.11401655

Cite this

Omar, N., Adesokan, A., & Madria, S. (2025). Leveraging Pre-Trained Language Models for Realistic Adversarial Attacks. In C.-Z. Xu, L. H. U, X. Cheng, J. Gao, G. Polese, H. Mei, P. Boniol, M. Tatsubori, C. Zhao, D. Zhou, & X. Hu (Eds.), Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025 (2025 ed., pp. 1514-1521). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData66926.2025.11401655

Leveraging Pre-Trained Language Models for Realistic Adversarial Attacks. / Omar, Nuzaer; Adesokan, Ademola; Madria, Sanjay.
Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025. ed. / Cheng-Zhong Xu; Leong Hou U; Xueqi Cheng; Jing Gao; Giuseppe Polese; Hong Mei; Paul Boniol; Michiaki Tatsubori; Chen Zhao; Dawei Zhou; Xiaohua Hu. 2025. ed. Institute of Electrical and Electronics Engineers Inc., 2025. p. 1514-1521.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Omar, N, Adesokan, A & Madria, S 2025, Leveraging Pre-Trained Language Models for Realistic Adversarial Attacks. in C-Z Xu, LH U, X Cheng, J Gao, G Polese, H Mei, P Boniol, M Tatsubori, C Zhao, D Zhou & X Hu (eds), Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025. 2025 edn, Institute of Electrical and Electronics Engineers Inc., pp. 1514-1521, 2025 IEEE International Conference on Big Data, BigData 2025, Macau, China, 12/8/25. https://doi.org/10.1109/BigData66926.2025.11401655

Omar N, Adesokan A, Madria S. Leveraging Pre-Trained Language Models for Realistic Adversarial Attacks. In Xu CZ, U LH, Cheng X, Gao J, Polese G, Mei H, Boniol P, Tatsubori M, Zhao C, Zhou D, Hu X, editors, Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025. 2025 ed. Institute of Electrical and Electronics Engineers Inc. 2025. p. 1514-1521 doi: 10.1109/BigData66926.2025.11401655

Omar, Nuzaer ; Adesokan, Ademola ; Madria, Sanjay. / Leveraging Pre-Trained Language Models for Realistic Adversarial Attacks. Proceedings - 2025 IEEE International Conference on Big Data, BigData 2025. editor / Cheng-Zhong Xu ; Leong Hou U ; Xueqi Cheng ; Jing Gao ; Giuseppe Polese ; Hong Mei ; Paul Boniol ; Michiaki Tatsubori ; Chen Zhao ; Dawei Zhou ; Xiaohua Hu. 2025. ed. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 1514-1521

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Leveraging Pre-Trained Language Models for Realistic Adversarial Attacks

Conference

ASJC Scopus Subject Areas

Keywords

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