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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
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Adversarial attacks and adversarial robustness in computational pathology. [PDF]
Nat Commun, 2022 AbstractArtificial 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. Hence, it is essential to quantify and mitigate this risk before widespread clinical use.
Ghaffari Laleh N +10 more
europepmc +5 more sources
Adversarial Attacks and Defenses
SIGKDD Explorations: Newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, 2021 Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions.
Mengnan Du, Huan Liu, Ruocheng Guo
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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.
Ajmal Mian, Naveed Akhtar
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Optical Adversarial Attack [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 ICCV Workshop ...
Abhiram Gnanasambandam +2 more
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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
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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
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
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Adversarial Attacks on Adversarial Bandits
CoRR, 2023 Accepted by ICLR ...
Yuzhe Ma, Zhijin Zhou
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A Survey on Universal Adversarial Attack [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 The intriguing phenomenon of adversarial examples has attracted significant attention in machine learning and what might be more surprising to the community is the existence of universal adversarial perturbations (UAPs), i.e. a single perturbation to fool the target DNN for most images.
Chaoning Zhang +5 more
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