Results 21 to 30 of about 3,276,744 (244)

A CMA-ES-Based Adversarial Attack on Black-Box Deep Neural Networks

IEEE Access, 2019
Deep neural networks(DNNs) are widely used in AI-controlled Cyber-Physical Systems (CPS) to controll cars, robotics, water treatment plants and railways.
Xiaohui Kuang, Hongyi Liu, Ye Wang, Qikun Zhang, Quanxin Zhang, Jun Zheng   +5 more
doaj   +1 more source

Attacking Black-Box Image Classifiers With Particle Swarm Optimization

IEEE Access, 2019
In order to better solve the shortcomings of Deep Neural Networks (DNNs) susceptible to adversarial examples, evaluating existing neural network classification performance and increasing training sets to improve the robustness of classification models ...
Quanxin Zhang, Kunqing Wang, Wenjiao Zhang, Jingjing Hu   +3 more
doaj   +1 more source

Hard-label based Small Query Black-box Adversarial Attack [PDF]

We consider the hard label based black box adversarial attack setting which solely observes predicted classes from the target model. Most of the attack methods in this setting suffer from impractical number of queries required to achieve a successful ...
Miller, Paul, McLaughlin, Niall, Park, Jeonghwan   +2 more
core   +3 more sources

Adv-Eye: A Transfer-Based Natural Eye Makeup Attack on Face Recognition

IEEE Access, 2023
Deep face recognition models are vulnerable to adversarial samples generated by adversarial attack methods. However, current attack methods do not adequately represent the security problems of the deep FR models, because they either produce adversarial ...
Jiatian Pi, Junyi Zeng, Quan Lu, Ning Jiang, Haiying Wu, Linchengxi Zeng, Zhiyou Wu   +6 more
doaj   +1 more source

Timing Black-Box Attacks: Crafting Adversarial Examples through Timing Leaks against DNNs on Embedded Devices

Transactions on Cryptographic Hardware and Embedded Systems, 2021
Deep neural networks (DNNs) have been applied to various industries. In particular, DNNs on embedded devices have attracted considerable interest because they allow real-time and distributed processing on site.
Tsunato Nakai, Daisuke Suzuki, Takeshi Fujino   +2 more
doaj   +1 more source

Intermediate-Layer Transferable Adversarial Attack With DNN Attention

IEEE Access, 2022
The widespread deployment of deep learning models in practice necessitates an assessment of their vulnerability, particularly in security-sensitive areas.
Shanshan Yang, Yu Yang, Linna Zhou, Rui Zhan, Yufei Man   +4 more
doaj   +1 more source

Accelerated surgery versus standard care in hip fracture (HIP ATTACK): an international, randomised, controlled trial

, 2020
Observational studies have suggested that accelerated surgery is associated with improved outcomes in patients with a hip fracture.
(HIP ATTACK Investigators), Landoni G.
core   +1 more source

Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks

IEEE Access, 2022
The field of adversarial machine learning has experienced a near exponential growth in the amount of papers being produced since 2018. This massive information output has yet to be properly processed and categorized.
Kaleel Mahmood, Rigel Mahmood, Ethan Rathbun, Marten van Dijk   +3 more
doaj   +1 more source

Black-Box Based Limited Query Membership Inference Attack

IEEE Access, 2022
Conventional membership inference attacks usually require a large number of queries of the target model when training shadow models, and this task becomes extremely difficult when the number of queries is limited.
Yu Zhang, Huaping Zhou, Pengyan Wang, Gaoming Yang   +3 more
doaj   +1 more source

G-MASK Facial Adversarial Attack Combining Gaussian Filtering and MASK [PDF]

Jisuanji gongcheng
The rapid development of deep neural networks has led to significant success in fields such as computer vision and natural language processing. However, adversarial attacks may inhibit the performance of neural networks, posing a serious threat to the ...
Qian LI, Haiyun XIANG, Yuting ZHANG, Yun GAN, Haode LIAO
doaj   +1 more source