Interpreting black-box models: a review on explainable artificial intelligence

V Hassija, V Chamola, A Mahapatra, A Singal… - Cognitive …, 2024‏ - Springer
Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based
methodological development in a broad range of domains. In this rapidly evolving field …

A survey on adversarial attacks and defences

A Chakraborty, M Alam, V Dey… - CAAI Transactions …, 2021‏ - Wiley Online Library
Deep learning has evolved as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

Interpreting adversarial examples in deep learning: A review

S Han, C Lin, C Shen, Q Wang, X Guan - ACM Computing Surveys, 2023‏ - dl.acm.org
Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

Adversarial weight perturbation helps robust generalization

D Wu, ST **a, Y Wang - Advances in neural information …, 2020‏ - proceedings.neurips.cc
The study on improving the robustness of deep neural networks against adversarial
examples grows rapidly in recent years. Among them, adversarial training is the most …

Deep learning for brain age estimation: A systematic review

M Tanveer, MA Ganaie, I Beheshti, T Goel, N Ahmad… - Information …, 2023‏ - Elsevier
Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

Adversarial attacks and defences: A survey

A Chakraborty, M Alam, V Dey… - arxiv preprint arxiv …, 2018‏ - arxiv.org
Deep learning has emerged as a strong and efficient framework that can be applied to a
broad spectrum of complex learning problems which were difficult to solve using the …

Wild patterns: Ten years after the rise of adversarial machine learning

B Biggio, F Roli - Proceedings of the 2018 ACM SIGSAC Conference on …, 2018‏ - dl.acm.org
Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …

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 …

[HTML][HTML] Review of artificial intelligence adversarial attack and defense technologies

S Qiu, Q Liu, S Zhou, C Wu - Applied Sciences, 2019‏ - mdpi.com
In recent years, artificial intelligence technologies have been widely used in computer
vision, natural language processing, automatic driving, and other fields. However, artificial …

Why do adversarial attacks transfer? explaining transferability of evasion and poisoning attacks

A Demontis, M Melis, M Pintor, M Jagielski… - 28th USENIX security …, 2019‏ - usenix.org
Transferability captures the ability of an attack against a machine-learning model to be
effective against a different, potentially unknown, model. Empirical evidence for …