DeepAI AI Chat
Log In Sign Up

Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution

by   Zongyi Li, et al.

Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models. However, there is a lack of systematic study on comparing different defense approaches under the same attacking setting. In this paper, we seek to fill the gap of systematic studies through comprehensive researches on understanding the behavior of neural text classifiers trained by various defense methods under representative adversarial attacks. In addition, we propose an effective method to further improve the robustness of neural text classifiers against such attacks and achieved the highest accuracy on both clean and adversarial examples on AGNEWS and IMDB datasets by a significant margin.


page 1

page 2

page 3

page 4


Detecting Adversarial Examples via Key-based Network

Though deep neural networks have achieved state-of-the-art performance i...

Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble

Despite neural networks have achieved prominent performance on many natu...

Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors

Most previous works usually explained adversarial examples from several ...

On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces

Recent studies have found that deep learning systems are vulnerable to a...

Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses

Convolutional Neural Networks have been shown to be vulnerable to advers...

PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning

Deep neural networks have demonstrated cutting edge performance on vario...

Textual Manifold-based Defense Against Natural Language Adversarial Examples

Recent studies on adversarial images have shown that they tend to leave ...