Results 51 to 60 of about 1,236,547 (109)

Adversarial Attack and Defense Strategies of Speaker Recognition Systems: A Survey

Electronics, 2022
Speaker recognition is a task that identifies the speaker from multiple audios. Recently, advances in deep learning have considerably boosted the development of speech signal processing techniques. Speaker or speech recognition has been widely adopted in
Hao Tan, Le Wang, Huan Zhang, Junjian Zhang, Muhammad Shafiq, Zhaoquan Gu   +5 more
semanticscholar   +1 more source

Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning

Applied Sciences, 2022
The ever-evolving cybersecurity environment has given rise to sophisticated adversaries who constantly explore new ways to attack cyberinfrastructure. Recently, the use of deep learning-based intrusion detection systems has been on the rise. This rise is
Kudzai Sauka, Gun-Yoo Shin, Dong-Wook Kim, Myung-Mook Han   +3 more
doaj   +1 more source

A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability

Computer Science Review, 2018
In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks.
Xiaowei Huang, D. Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, Xinping Yi   +7 more
semanticscholar   +1 more source

Adversarial Attacks to Manipulate Target Localization of Object Detector

IEEE Access
Adversarial attack has gradually become an important branch in the field of artificial intelligence security, where the potential threat brought by adversarial example attack is more not to be ignored.
Kai Xu, Xiao Cheng, Ji Qiao, Jia-Teng Li, Kai-Xuan Ji, Jia-Yong Zhong, Peng Tian, Jian-Xun Mi   +7 more
doaj   +1 more source

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

A Survey on Adversarial Attack in the Age of Artificial Intelligence

Wireless Communications and Mobile Computing, 2021
With the rapid evolution of the Internet, the application of artificial intelligence fields is more and more extensive, and the era of AI has come. At the same time, adversarial attacks in the AI field are also frequent.
Zixiao Kong, Jingfeng Xue, Yong Wang, Lu Huang, Zequn Niu, Feng Li   +5 more
semanticscholar   +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

Multi-Level Chinese Adversarial Example Generation Method Based on Glyph and Semantic [PDF]

Jisuanji gongcheng
Deep neural network language models are vulnerable to adversarial example attacks during application. To address this issue, adversarial examples are typically generated by adding minor perturbations to the original samples to mislead the model into ...
SUN Yu, WANG Hongjie, DU Yanhui, LIU Nan
doaj   +1 more source