2019

Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey

Zhang, Wei Emma, Sheng, Quan Z., Alhazmi, Ahoud et al.

Understand

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications.

However, previous efforts have shown that DNNs were vulnerable to strategically modified samples, named adversarial examples.
These samples are generated with some imperceptible perturbations but can fool the DNNs to give false predictions.
Inspired by the popularity of generating adversarial examples for image DNNs, research efforts on attacking DNNs for textual applications emerges in recent years.

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Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018 · 2018
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Adaptive Adversarial Attack on Scene Text Recognition
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TextBugger: Generating Adversarial Text Against Real-world Applications. In Proc. of 26th Annual Network and Distributed System Security Symposium (NDSS 2019) . San Diego, California, USA
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Adversarial Examples for Evaluating Reading Comprehension Systems. In Proc. of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017) . Copenhagen, Denmark, 2021–2031
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. In Proc. of the 32nd International Conference on Machine Learning (ICML 2015) . Lille, France, 2048–2057
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