Explainable artificial intelligence applications in cyber security: State-of-the-art in research

Z Zhang, H Al Hamadi, E Damiani, CY Yeun… - IEEE …, 2022 - ieeexplore.ieee.org
This survey presents a comprehensive review of current literature on Explainable Artificial
Intelligence (XAI) methods for cyber security applications. Due to the rapid development of …

A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability

X Huang, D Kroening, W Ruan, J Sharp, Y Sun… - Computer Science …, 2020 - Elsevier
In the past few years, significant progress has been made on deep neural networks (DNNs)
in achieving human-level performance on several long-standing tasks. With the broader …

[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

S Ali, T Abuhmed, S El-Sappagh, K Muhammad… - Information fusion, 2023 - Elsevier
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Robustbench: a standardized adversarial robustness benchmark

F Croce, M Andriushchenko, V Sehwag… - arXiv preprint arXiv …, 2020 - arxiv.org
As a research community, we are still lacking a systematic understanding of the progress on
adversarial robustness which often makes it hard to identify the most promising ideas in …

On adaptive attacks to adversarial example defenses

F Tramer, N Carlini, W Brendel… - Advances in neural …, 2020 - proceedings.neurips.cc
Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to
adversarial examples. We find, however, that typical adaptive evaluations are incomplete …

Machine learning testing: Survey, landscapes and horizons

JM Zhang, M Harman, L Ma… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

Unlabeled data improves adversarial robustness

Y Carmon, A Raghunathan, L Schmidt… - Advances in neural …, 2019 - proceedings.neurips.cc
We demonstrate, theoretically and empirically, that adversarial robustness can significantly
benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of …

[HTML][HTML] Adversarial attacks and defenses in deep learning

K Ren, T Zheng, Z Qin, X Liu - Engineering, 2020 - Elsevier
With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques,
it is critical to ensure the security and robustness of the deployed algorithms. Recently, the …

Hopskipjumpattack: A query-efficient decision-based attack

J Chen, MI Jordan… - 2020 ieee symposium on …, 2020 - ieeexplore.ieee.org
The goal of a decision-based adversarial attack on a trained model is to generate
adversarial examples based solely on observing output labels returned by the targeted …

Benchmarking adversarial robustness on image classification

Y Dong, QA Fu, X Yang, T Pang… - proceedings of the …, 2020 - openaccess.thecvf.com
Deep neural networks are vulnerable to adversarial examples, which becomes one of the
most important research problems in the development of deep learning. While a lot of efforts …