# AED: An black-box NLP classifier model attacker

arXiv: https://arxiv.org/abs/2112.11660 · HTML: https://mlsanity.com/paper/2112.11660/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 38 references that are in the map.
- First posted on arXiv 22 Dec 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (2018)
- [Adversarial Attacks on Neural Network Policies](https://mlsanity.com/paper/1702.02284/) (2017)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019)
- [Long Short-Term Memory-Networks for Machine Reading](https://mlsanity.com/paper/1601.06733/) (2016)
- [Attention Branch Network: Learning of Attention Mechanism for Visual Explanation](https://mlsanity.com/paper/1812.10025/) (2018)
- [Deep Text Classification Can be Fooled](https://mlsanity.com/paper/1704.08006/) (2017)
- [Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization](https://mlsanity.com/paper/1908.00748/) (2019)
- [Towards Crafting Text Adversarial Samples](https://mlsanity.com/paper/1707.02812/) (2017)
- [How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?](https://mlsanity.com/paper/1704.04441/) (2017)
- [Deep Semantic Role Labeling with Self-Attention](https://mlsanity.com/paper/1712.01586/) (2017)
- [Post-hoc explanation of black-box classifiers using confident itemsets](https://mlsanity.com/paper/2005.01992/) (2020)
- [Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack](https://mlsanity.com/paper/2109.02229/) (2021)
- [Measuring Sentences Similarity: A Survey](https://mlsanity.com/paper/1910.03940/) (2019)

## Abstract (arXiv)

Deep Neural Networks (DNNs) have been successful in solving real-world tasks in domains such as connected and automated vehicles, disease, and job hiring. However, their implications are far-reaching in critical application areas. Hence, there is a growing concern regarding the potential bias and robustness of these DNN models. A transparency and robust model is always demanded in high-stakes domains where reliability and safety are enforced, such as healthcare and finance. While most studies have focused on adversarial image attack scenarios, fewer studies have investigated the robustness of DNN models in natural language processing (NLP) due to their adversarial samples are difficult to generate. To address this gap, we propose a word-level NLP classifier attack model called "AED," which stands for Attention mechanism enabled post-model Explanation with Density peaks clustering algorithm for synonyms search and substitution. AED aims to test the robustness of NLP DNN models by interpretability their weaknesses and exploring alternative ways to optimize them. By identifying vulnerabilities and providing explanations, AED can help improve the reliability and safety of DNN models in critical application areas such as healthcare and automated transportation. Our experiment results demonstrate that compared with other existing models, AED can effectively generate adversarial examples that can fool the victim model while maintaining the original meaning of the input.