A survey of adversarial defenses and robustness in nlp
In the past few years, it has become increasingly evident that deep neural networks are not
resilient enough to withstand adversarial perturbations in input data, leaving them …
resilient enough to withstand adversarial perturbations in input data, leaving them …
Robust natural language processing: Recent advances, challenges, and future directions
Recent natural language processing (NLP) techniques have accomplished high
performance on benchmark data sets, primarily due to the significant improvement in the …
performance on benchmark data sets, primarily due to the significant improvement in the …
Adversarial glue: A multi-task benchmark for robustness evaluation of language models
Large-scale pre-trained language models have achieved tremendous success across a
wide range of natural language understanding (NLU) tasks, even surpassing human …
wide range of natural language understanding (NLU) tasks, even surpassing human …
Hidden trigger backdoor attack on {NLP} models via linguistic style manipulation
The vulnerability of deep neural networks (DNN) to backdoor (trojan) attacks is extensively
studied for the image domain. In a backdoor attack, a DNN is modified to exhibit expected …
studied for the image domain. In a backdoor attack, a DNN is modified to exhibit expected …
“real attackers don't compute gradients”: bridging the gap between adversarial ml research and practice
Recent years have seen a proliferation of research on adversarial machine learning.
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
Numerous papers demonstrate powerful algorithmic attacks against a wide variety of …
Risk taxonomy, mitigation, and assessment benchmarks of large language model systems
Large language models (LLMs) have strong capabilities in solving diverse natural language
processing tasks. However, the safety and security issues of LLM systems have become the …
processing tasks. However, the safety and security issues of LLM systems have become the …
Towards a robust deep neural network against adversarial texts: A survey
Deep neural networks (DNNs) have achieved remarkable success in various tasks (eg,
image classification, speech recognition, and natural language processing (NLP)). However …
image classification, speech recognition, and natural language processing (NLP)). However …
Query-efficient adversarial attack with low perturbation against end-to-end speech recognition systems
S Wang, Z Zhang, G Zhu, X Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With the widespread use of automated speech recognition (ASR) systems in modern
consumer devices, attack against ASR systems have become an attractive topic in recent …
consumer devices, attack against ASR systems have become an attractive topic in recent …
Neuronfair: Interpretable white-box fairness testing through biased neuron identification
Deep neural networks (DNNs) have demonstrated their outperformance in various domains.
However, it raises a social concern whether DNNs can produce reliable and fair decisions …
However, it raises a social concern whether DNNs can produce reliable and fair decisions …
A systematic review of machine learning algorithms in cyberbullying detection: future directions and challenges
M Arif - Journal of Information Security and Cybercrimes …, 2021 - journals.nauss.edu.sa
Social media networks are becoming an essential part of life for most of the world's
population. Detecting cyberbullying using machine learning and natural language …
population. Detecting cyberbullying using machine learning and natural language …