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 …
Adversarial machine learning in wireless communications using RF data: A review
D Adesina, CC Hsieh, YE Sagduyu… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …
complex tasks involved in wireless communications. Supported by recent advances in …
Adversarial attack and defense technologies in natural language processing: A survey
S Qiu, Q Liu, S Zhou, W Huang - Neurocomputing, 2022 - Elsevier
Recently, the adversarial attack and defense technology has made remarkable
achievements and has been widely applied in the computer vision field, promoting its rapid …
achievements and has been widely applied in the computer vision field, promoting its rapid …
Towards robustness against natural language word substitutions
Robustness against word substitutions has a well-defined and widely acceptable form, ie,
using semantically similar words as substitutions, and thus it is considered as a fundamental …
using semantically similar words as substitutions, and thus it is considered as a fundamental …
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 …
Searching for an effective defender: Benchmarking defense against adversarial word substitution
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 examples, and various methods have been proposed to defend against …
Certified robustness to text adversarial attacks by randomized [mask]
Very recently, few certified defense methods have been developed to provably guarantee
the robustness of a text classifier to adversarial synonym substitutions. However, all the …
the robustness of a text classifier to adversarial synonym substitutions. However, all the …
Grey-box adversarial attack and defence for sentiment classification
We introduce a grey-box adversarial attack and defence framework for sentiment
classification. We address the issues of differentiability, label preservation and input …
classification. We address the issues of differentiability, label preservation and input …
Transferable multimodal attack on vision-language pre-training models
Abstract Vision-Language Pre-training (VLP) models have achieved remarkable success in
practice, while easily being misled by adversarial attack. Though harmful, adversarial …
practice, while easily being misled by adversarial attack. Though harmful, adversarial …
Certified robustness to word substitution attack with differential privacy
The robustness and security of natural language processing (NLP) models are significantly
important in real-world applications. In the context of text classification tasks, adversarial …
important in real-world applications. In the context of text classification tasks, adversarial …