Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey [PDF]
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
doaj +2 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
A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
Future InternetWith the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem.
Hassan Khazane +3 more
doaj +2 more sources
Adversarial attacks on medical machine learning. [PDF]
Science, 2019 Emerging vulnerabilities demand new conversations With public and academic attention increasingly focused on the new role of machine learning in the health information economy, an unusual and no-longer-esoteric category of vulnerabilities in machine ...
Finlayson SG +5 more
europepmc +2 more sources
Baseline Defenses for Adversarial Attacks Against Aligned Language Models [PDF]
arXiv.org, 2023 As Large Language Models quickly become ubiquitous, it becomes critical to understand their security vulnerabilities. Recent work shows that text optimizers can produce jailbreaking prompts that bypass moderation and alignment. Drawing from the rich body
Neel Jain +9 more
semanticscholar +1 more source
Enhancing the Transferability of Adversarial Attacks through Variance Tuning [PDF]
Computer Vision and Pattern Recognition, 2021 Deep neural networks are vulnerable to adversarial examples that mislead the models with imperceptible perturbations. Though adversarial attacks have achieved incredible success rates in the white-box setting, most existing adversaries often exhibit weak
Xiaosen Wang, Kun He
semanticscholar +1 more source
Feature Importance-aware Transferable Adversarial Attacks [PDF]
IEEE International Conference on Computer Vision, 2021 Transferability of adversarial examples is of central importance for attacking an unknown model, which facilitates adversarial attacks in more practical scenarios, e.g., black-box attacks.
Zhibo Wang +5 more
semanticscholar +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
doaj +1 more source
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
Admix: Enhancing the Transferability of Adversarial Attacks [PDF]
IEEE International Conference on Computer Vision, 2021 Deep neural networks are known to be extremely vulnerable to adversarial examples under white-box setting. Moreover, the malicious adversaries crafted on the surrogate (source) model often exhibit black-box transferability on other models with the same ...
Xiaosen Wang +3 more
semanticscholar +1 more source