Results 71 to 80 of about 1,236,597 (154)

Functional Adversarial Attacks

CoRR, 2019
Accepted to NeurIPS ...
Cassidy Laidlaw, Soheil Feizi
openaire   +3 more sources

Adversarial Attack Transferability Enhancement Algorithm Based on Input Channel Splitting [PDF]

Jisuanji gongcheng, 2023
The Deep Neural Network(DNN) has been widely used in face recognition, automatic driving, and other scenarios;however, it is vulnerable to attacks by adversarial samples.Methods by which adversarial samples are generated can be classified into white-box ...
ZHENG Desheng, CHEN Jixin, ZHOU Jing, KE Wuping, LU Chao, ZHOU Yong, QIU Qian
doaj   +1 more source

A Survey on Physical Adversarial Attack in Computer Vision [PDF]

arXiv.org, 2022
Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-craft feature extraction with its strong feature learning capability, leading to substantial enhancements in traditional tasks.
Donghua Wang, Wen Yao, Tingsong Jiang, Guijian Tang, Xiaoqian Chen   +4 more
semanticscholar   +1 more source

Distributionally Adversarial Attack

Proceedings of the AAAI Conference on Artificial Intelligence, 2019
Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a data distribution p(x), typically ...
Tianhang Zheng, Changyou Chen, Kui Ren 0001   +2 more
openaire   +3 more sources

Adversarial attacks against supervised machine learning based network intrusion detection systems.

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, Daniyal Alghazzawi, Reem Alotaibi, Osama Rabie   +3 more
doaj   +2 more sources

Imperceptible Adversarial Attack via Invertible Neural Networks [PDF]

AAAI Conference on Artificial Intelligence, 2022
Adding perturbations via utilizing auxiliary gradient information or discarding existing details of the benign images are two common approaches for generating adversarial examples.
Zihan Chen, Zifan Wang, Junjie Huang, Wentao Zhao, Xiao Liu, Dejian Guan   +5 more
semanticscholar   +1 more source

Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector

IEEE Access, 2023
Deep neural networks (DNNs) are vulnerable to adversarial attacks. In particular, object detectors may be attacked by applying a particular adversarial patch to the image.
Xiaochun Lei, Xiang Cai, Chang Lu, Zetao Jiang, Zhaoting Gong, Linjun Lu   +5 more
doaj   +1 more source

Real-Time Adversarial Attacks [PDF]

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019
In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail. While these attack methods are very effective, they only focus on scenarios where the target model takes static input, i.e., an attacker can ...
Yuan Gong 0001, Boyang Li 0003, Christian Poellabauer, Yiyu Shi 0001   +3 more
openaire   +2 more sources

Multi-Targeted Adversarial Example in Evasion Attack on Deep Neural Network

IEEE Access, 2018
Deep neural networks (DNNs) are widely used for image recognition, speech recognition, pattern analysis, and intrusion detection. Recently, the adversarial example attack, in which the input data are only slightly modified, although not an issue for ...
Hyun Kwon, Yongchul Kim, Ki-Woong PARK, Hyunsoo Yoon, Daeseon Choi   +4 more
doaj   +1 more source

Multi-Stage Adversarial Defense for Online DDoS Attack Detection System in IoT

IEEE Access
Machine learning-based Distributed Denial of Service (DDoS) attack detection systems have proven effective in detecting and preventing DDoD attacks in Internet of Things (IoT) systems.
Yonas Kibret Beshah, Surafel Lemma Abebe, Henock Mulugeta Melaku   +2 more
doaj   +1 more source