Results 51 to 60 of about 5,561,446 (302)

Adversarial Example Games

, 2020
Appears in: Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
Bose, Avishek Joey, Gidel, Gauthier, Berard, Hugo, Cianflone, Andre, Vincent, Pascal, Lacoste-Julien, Simon, Hamilton, William L.   +6 more
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Offense and defence against adversarial sample: A reinforcement learning method in energy trading market

Frontiers in Energy Research, 2023
The energy trading market that can support free bidding among electricity users is currently the key method in smart grid demand response. Reinforcement learning is used to formulate optimal strategies for them to obtain optimal strategies. Non-etheless,
Donghe Li, Qingyu Yang, Qingyu Yang, Linyue Ma, Zhenhua Peng, Xiao Liao   +5 more
doaj   +1 more source

Adversarial Examples Detection Beyond Image Space [PDF]

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
To appear in ICASSP ...
Chen, Kejiang, Chen, Yuefeng, Zhou, Hang, Qin, Chuan, Mao, Xiaofeng, Zhang, Weiming, Yu, Nenghai   +6 more
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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

Image Classification Adversarial Example Defense Method Based on Conditional Diffusion Model [PDF]

Jisuanji gongcheng
Deep-learning models have achieved impressive results in fields such as image classification; however, they remain vulnerable to interference and threats from adversarial examples.
CHEN Zimin, GUAN Zhitao
doaj   +1 more source

Are adversarial examples inevitable?

, 2018
ISBN:978-1-7138-7273 ...
Shafahi, Ali, Huang, W. Ronny, Studer, Christoph; id_orcid0000-0001-8950-6267, Feizi, Soheil, Goldstein, Tom   +4 more
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POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI

IEEE Access
Adversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence.
Han-Ju Lee, Jin-Seoung Kim, Han-Jin Lee, Seok-Hwan Choi   +3 more
doaj   +1 more source

Adversarial Example Detection by Classification for Deep Speech Recognition [PDF]

IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019
Machine Learning systems are vulnerable to adversarial attacks and will highly likely produce incorrect outputs under these attacks. There are white-box and black-box attacks regarding to adversary’s access level to the victim learning algorithm.
Saeid Samizade, Zheng-Hua Tan, Chao Shen, X. Guan   +3 more
semanticscholar   +1 more source

Downstream-agnostic Adversarial Examples

2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023
This paper has been accepted by the International Conference on Computer Vision (ICCV '23, October 2--6, 2023, Paris, France)
Zhou, Ziqi, Hu, Shengshan, Zhao, Ruizhi, Wang, Qian, Zhang, Leo Yu, Hou, Junhui, Jin, Hai   +6 more
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Distinguishability of adversarial examples [PDF]

Proceedings of the 15th International Conference on Availability, Reliability and Security, 2020
Machine learning models can be easily fooled by adversarial examples which are generated from clean examples with small perturbations. This poses a critical challenge to machine learning security, and impedes the wide application of machine learning in many important domains such as computer vision and malware detection. From a unique angle, we propose
Yi Qin, Ryan Hunt, Chuan Yue
openaire   +1 more source