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Task-driven attention attack: An enhanced adversarial framework for long text

delete2025-06-29
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PRE
AI
Q
Q. N. Xu
J
Jianxin Bi
J
Jingyuan Liu
DOI:10.1016/j.eswa.2025.128834delete
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Abstract

Abstract

En 中文
• Existing attacks fail on long texts due to inefficient scoring mechanism. • TDAA: Novel task-driven attack framework for long texts. • Integrates contextual semantics & task-specific attention. • Adaptive length threshold + statistically non-essential sentence elimination. • 34.7 % ↑ ASR & 40 % ↑ efficiency vs baselines.

Journal

Expert Systems with Applications cover
Expert Systems with Applications
IF:
7.5
Papers:
3.0W
Citations:
10.2W

Organization

No organization information available
Cited Papers

Cited Papers

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TextJuggler: Fooling text classification tasks by generating high-quality adversarial examples
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errPeng, Hao; Wang, Zhe; Wei, Chao; Zhao, Dandan; Xu, Guangquan; Han, Jianming; Guo, Shixin; Zhong, Ming; Ji, Shouling
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