Explainable artificial intelligence applications in cyber security: State-of-the-art in research
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 …
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
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 …
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
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 …
applications, but the outcomes of many AI models are challenging to comprehend and trust …
Robustbench: a standardized adversarial robustness benchmark
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 …
adversarial robustness which often makes it hard to identify the most promising ideas in …
On adaptive attacks to adversarial example defenses
Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to
adversarial examples. We find, however, that typical adaptive evaluations are incomplete …
adversarial examples. We find, however, that typical adaptive evaluations are incomplete …
Machine learning testing: Survey, landscapes and horizons
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 …
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
Unlabeled data improves adversarial robustness
We demonstrate, theoretically and empirically, that adversarial robustness can significantly
benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of …
benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of …
[HTML][HTML] Adversarial attacks and defenses in deep learning
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 …
it is critical to ensure the security and robustness of the deployed algorithms. Recently, the …
Hopskipjumpattack: A query-efficient decision-based attack
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 …
adversarial examples based solely on observing output labels returned by the targeted …
Benchmarking adversarial robustness on image classification
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 …
most important research problems in the development of deep learning. While a lot of efforts …