Search Results for author:
Found 5 papers, 2 papers with code
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations
Adversarial attacks curated against NLP models are increasingly becoming practical threats.
TCAB: A Large-Scale Text Classification Attack Benchmark
In addition to the primary tasks of detecting and labeling attacks, TCAB can also be used for attack localization, attack target labeling, and attack characterization.
Identifying Adversarial Attacks on Text Classifiers
The landscape of adversarial attacks against text classifiers continues to grow, with new attacks developed every year and many of them available in standard toolkits, such as TextAttack and OpenAttack.
An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness
We demonstrate that training the networks to have interpretable gradients improves their robustness to adversarial perturbations.
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks
The problem of explaining deep learning models, and model predictions generally, has attracted intensive interest recently.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
