Results 1 to 10 of about 17,780 (281)
Adversarial attacks and adversarial robustness in computational pathology. [PDF]
Nat Commun, 2022 Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis and providing biomarkers directly from routine pathology slides. However, AI applications are vulnerable to adversarial attacks.
Ghaffari Laleh N +10 more
europepmc +6 more sources
Adversarial attacks against supervised machine learning based network intrusion detection systems. [PDF]
PLoS ONE, 2022 Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the ...
Ebtihaj Alshahrani +3 more
doaj +3 more sources
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
IEEE Access, 2018 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 to surveillance and security.
Naveed Akhtar, Ajmal Mian
exaly +3 more sources
Multiple Adversarial Domains Adaptation Approach for Mitigating Adversarial Attacks Effects
International Transactions on Electrical Energy Systems, 2022 Although neural networks are near achieving performance similar to humans in many tasks, they are susceptible to adversarial attacks in the form of a small, intentionally designed perturbation, which could lead to misclassifications.
Bader Rasheed +4 more
doaj +2 more sources
Optical Adversarial Attack [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 ICCV Workshop ...
Abhiram Gnanasambandam +2 more
openaire +2 more sources
Adversarial Attacks on Hyperbolic Networks
As hyperbolic deep learning grows in popularity, so does the need for adversarial robustness in the context of such a non-Euclidean geometry. To this end, this paper proposes hyperbolic alternatives to the commonly used FGM and PGD adversarial attacks ...
Zahálka, Jan +2 more
core +3 more sources
Black Box Adversarial Attack Starting Point Promotion Method Based on Mobility Between Models [PDF]
Jisuanji gongcheng, 2021 In order to efficiently find the adversarial samples under the decision-based black box attacks, a method using the mobility between models is proposed to enhance the adversarial starting point. The mobility is used to circularly superimpose interference
CHEN Xiaonan, HU Jianmin, ZHANG Benjun, CHEN Ailing
doaj +1 more source
On the Reversibility of Adversarial Attacks
2021 IEEE International Conference on Image Processing (ICIP), 2021 Adversarial attacks modify images with perturbations that change the prediction of classifiers. These modified images, known as adversarial examples, expose the vulnerabilities of deep neural network classifiers. In this paper, we investigate the predictability of the mapping between the classes predicted for original images and for their corresponding
Chau Yi Li +4 more
openaire +2 more sources
A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
Applied Sciences, 2021 Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make
Zhirui Luo, Qingqing Li, Jun Zheng
doaj +1 more source
Adversarial Attacks on Adversarial Bandits
CoRR, 2023 Accepted by ICLR ...
Yuzhe Ma, Zhijin Zhou
openaire +3 more sources