Results 61 to 70 of about 543,217 (204)

DTFA: Adversarial attack with discrete cosine transform noise and target features on deep neural networks

IET Image Processing, 2023
Image recognition on deep neural network is vulnerable to adversarial sample attacks. The adversarial attack accuracy is low when only limited queries on the target are allowed with the current black box environment.
Dong Yang, Wei Chen, Songjie Wei
doaj   +1 more source

Attack Tree Analysis for Adversarial Evasion Attacks [PDF]

, 2023
Recently, the evolution of deep learning has promoted the application of machine learning (ML) to various systems. However, there are ML systems, such as autonomous vehicles, that cause critical damage when they misclassify.
Yamaguchi, Yuki, Aoki, Toshiaki
core  

Transferable Sparse Adversarial Attack

CoRR, 2021
Deep neural networks have shown their vulnerability to adversarial attacks. In this paper, we focus on sparse adversarial attack based on the $\ell_0$ norm constraint, which can succeed by only modifying a few pixels of an image. Despite a high attack success rate, prior sparse attack methods achieve a low transferability under the black-box protocol ...
Ziwen He, Wei Wang 0025, Jing Dong 0003, Tieniu Tan   +3 more
openaire   +3 more sources

A Simple Yet Efficient Method for Adversarial Word-Substitute Attack [PDF]

, 2022
NLP researchers propose different word-substitute black-box attacks that can fool text classification models. In such attack, an adversary keeps sending crafted adversarial queries to the target model until it can successfully achieve the intended ...
Li, Tianle, Yang, Yi
core   +1 more source

AB-VIP: Attack Method Using Brightness Information for Visibility Improvement of Adversarial Perturbations on the NMS Algorithm

IEEE Access
As with classification models, object detection models are vulnerable to adversarial attacks. In particular, adversarial attacks on key components of object detection models such as Region Proposal Network (RPN) and Non-Maximum Suppression (NMS ...
Gwang-Nam Kim, Han-Ju Lee, Han-Jin Lee, Hyeon-Jin Jung, Seok-Hwan Choi   +4 more
doaj   +1 more source

A Two-Stage Generative Adversarial Networks With Semantic Content Constraints for Adversarial Example Generation

IEEE Access, 2020
Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples, and these manipulated instances can mislead DNN ...
Jianyi Liu, Yu Tian, Ru Zhang, Youqiang Sun, Chan Wang   +4 more
doaj   +1 more source

A Distributed Biased Boundary Attack Method in Black-Box Attack

Applied Sciences, 2021
The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios.
Fengtao Xiang, Jiahui Xu, Wanpeng Zhang, Weidong Wang   +3 more
doaj   +1 more source

Towards Adversarial Attack on Vision-Language Pre-training Models

, 2022
While vision-language pre-training model (VLP) has shown revolutionary improvements on various vision-language (V+L) tasks, the studies regarding its adversarial robustness remain largely unexplored.
Sang, Jitao, Zhang, Jiaming, Yi, Qi
core  

Wasserstein Adversarial Robustness [PDF]

, 2020
Deep models, while being extremely flexible and accurate, are surprisingly vulnerable to ``small, imperceptible'' perturbations known as adversarial attacks.
Wu, Kaiwen
core  

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