Adversarial deep learning: A survey on adversarial attacks and defense mechanisms on image classification
The popularity of adapting deep neural networks (DNNs) in solving hard problems has
increased substantially. Specifically, in the field of computer vision, DNNs are becoming a …
increased substantially. Specifically, in the field of computer vision, DNNs are becoming a …
Universal adversarial attacks on deep neural networks for medical image classification
H Hirano, A Minagi, K Takemoto - BMC medical imaging, 2021 - Springer
Abstract Background Deep neural networks (DNNs) are widely investigated in medical
image classification to achieve automated support for clinical diagnosis. It is necessary to …
image classification to achieve automated support for clinical diagnosis. It is necessary to …
Attacking deep reinforcement learning with decoupled adversarial policy
While Deep Reinforcement Learning (DRL) has achieved outstanding performance in
extensive applications, exploiting its vulnerability with adversarial attacks is essential …
extensive applications, exploiting its vulnerability with adversarial attacks is essential …
Adversarial robustness assessment: Why in evaluation both L0 and L∞ attacks are necessary
There are different types of adversarial attacks and defences for machine learning
algorithms which makes assessing the robustness of an algorithm a daunting task …
algorithms which makes assessing the robustness of an algorithm a daunting task …
Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks
H Hirano, K Koga, K Takemoto - Plos one, 2020 - journals.plos.org
Owing the epidemic of the novel coronavirus disease 2019 (COVID-19), chest X-ray
computed tomography imaging is being used for effectively screening COVID-19 patients …
computed tomography imaging is being used for effectively screening COVID-19 patients …
Universal adversarial perturbations for CNN classifiers in EEG-based BCIs
Objective. Multiple convolutional neural network (CNN) classifiers have been proposed for
electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN …
electroencephalogram (EEG) based brain-computer interfaces (BCIs). However, CNN …
A simple and strong baseline for universal targeted attacks on Siamese visual tracking
Siamese trackers are shown to be vulnerable to adversarial attacks recently. However, the
existing attack methods craft the perturbations for each video independently, which comes at …
existing attack methods craft the perturbations for each video independently, which comes at …
Natural images allow universal adversarial attacks on medical image classification using deep neural networks with transfer learning
A Minagi, H Hirano, K Takemoto - Journal of Imaging, 2022 - mdpi.com
Transfer learning from natural images is used in deep neural networks (DNNs) for medical
image classification to achieve a computer-aided clinical diagnosis. Although the …
image classification to achieve a computer-aided clinical diagnosis. Although the …
Backdoor attacks to deep neural network-based system for COVID-19 detection from chest X-ray images
Y Matsuo, K Takemoto - Applied Sciences, 2021 - mdpi.com
Open-source deep neural networks (DNNs) for medical imaging are significant in emergent
situations, such as during the pandemic of the 2019 novel coronavirus disease (COVID-19) …
situations, such as during the pandemic of the 2019 novel coronavirus disease (COVID-19) …
A reading survey on adversarial machine learning: Adversarial attacks and their understanding
S Kotyan - arxiv preprint arxiv:2308.03363, 2023 - arxiv.org
Deep Learning has empowered us to train neural networks for complex data with high
performance. However, with the growing research, several vulnerabilities in neural networks …
performance. However, with the growing research, several vulnerabilities in neural networks …