Explainable artificial intelligence: a comprehensive review

D Minh, HX Wang, YF Li, TN Nguyen - Artificial Intelligence Review, 2022 - Springer
Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …

Advances in adversarial attacks and defenses in computer vision: A survey

N Akhtar, A Mian, N Kardan, M Shah - IEEE Access, 2021 - ieeexplore.ieee.org
Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Improving adversarial transferability via neuron attribution-based attacks

J Zhang, W Wu, J Huang, Y Huang… - Proceedings of the …, 2022 - openaccess.thecvf.com
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. It is thus
imperative to devise effective attack algorithms to identify the deficiencies of DNNs …

Feature importance-aware transferable adversarial attacks

Z Wang, H Guo, Z Zhang, W Liu… - Proceedings of the …, 2021 - openaccess.thecvf.com
Transferability of adversarial examples is of central importance for attacking an unknown
model, which facilitates adversarial attacks in more practical scenarios, eg, blackbox attacks …

Threat of adversarial attacks on deep learning in computer vision: A survey

N Akhtar, A Mian - Ieee Access, 2018 - ieeexplore.ieee.org
Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Skip connections matter: On the transferability of adversarial examples generated with resnets

D Wu, Y Wang, ST Xia, J Bailey, X Ma - arXiv preprint arXiv:2002.05990, 2020 - arxiv.org
Skip connections are an essential component of current state-of-the-art deep neural
networks (DNNs) such as ResNet, WideResNet, DenseNet, and ResNeXt. Despite their …

Toward understanding and boosting adversarial transferability from a distribution perspective

Y Zhu, Y Chen, X Li, K Chen, Y He… - … on Image Processing, 2022 - ieeexplore.ieee.org
Transferable adversarial attacks against Deep neural networks (DNNs) have received broad
attention in recent years. An adversarial example can be crafted by a surrogate model and …

Universal adversarial examples in remote sensing: Methodology and benchmark

Y Xu, P Ghamisi - IEEE Transactions on Geoscience and …, 2022 - ieeexplore.ieee.org
Deep neural networks have achieved great success in many important remote sensing
tasks. Nevertheless, their vulnerability to adversarial examples should not be neglected. In …

On success and simplicity: A second look at transferable targeted attacks

Z Zhao, Z Liu, M Larson - Advances in Neural Information …, 2021 - proceedings.neurips.cc
Achieving transferability of targeted attacks is reputed to be remarkably difficult. The current
state of the art has resorted to resource-intensive solutions that necessitate training model …

A survey on adversarial attacks in computer vision: Taxonomy, visualization and future directions

T Long, Q Gao, L Xu, Z Zhou - Computers & Security, 2022 - Elsevier
Deep learning has been widely applied in various fields such as computer vision, natural
language processing, and data mining. Although deep learning has achieved significant …