Results 231 to 240 of about 86,248 (260)

An AI-driven conceptual framework for detecting fake news and deepfake content: a systematic review. [PDF]

Front Artif Intell
Moyo BV, Tuyikeze T, Matsebula F, Obagbuwa IC.   +3 more
europepmc   +1 more source

SecMLOps: A comprehensive framework for integrating security throughout the machine learning operations lifecycle. [PDF]

Empir Softw Eng
Zhang X, Zhao P, Jaskolka J, Li H, Lu R.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Boosting adversarial robustness via self-paced adversarial training

Neural Networks, 2023
Adversarial training is considered one of the most effective methods to improve the adversarial robustness of deep neural networks. Despite the success, it still suffers from unsatisfactory performance and overfitting. Considering the intrinsic mechanism of adversarial training, recent studies adopt the idea of curriculum learning to alleviate ...
Lirong He, Qingzhong Ai, Xincheng Yang, Yazhou Ren, Qifan Wang, Zenglin Xu   +5 more
openaire   +4 more sources

Robust generative adversarial network

Machine Learning, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, Shufei, Qian, Zhuang, Huang, Kaizhu, Zhang, Rui, Xiao, Jimin, He, Yuan, Lu, Canyi   +6 more
openaire   +2 more sources

Robustness for Adversarial Risk Analysis

2016
Adversarial Risk Analysis is an emergent paradigm for supporting a decision maker who faces adversaries in problems in which the consequences are random and depend on the actions of all participating agents. In this chapter, we outline a framework for robust analysis methods in Adversarial Risk Analysis. Our discussion focuses on security applications.
D Rios Insua, F Ruggeri, C Alfaro, J Gomez   +3 more
openaire   +3 more sources

Adversarially Robust Hypothesis Testing

2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019
In this paper, we investigate the adversarial robustness of classification problems. In the considered model, after a sample is generated, it will be modified by an adversary before being observed by the classifier. The classifier needs to decide the underlying hypothesis that generates the sample from the adversarially modified data. We formulate this
Yulu Jin, Lifeng Lai
openaire   +1 more source

Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey

IEEE Communications Surveys and Tutorials, 2023
Yulong Wang, Xin Yuan, Wei Ni
exaly  

A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security

IEEE Communications Surveys and Tutorials, 2023
Senming Yan, Jing Ren, Wei Wang
exaly  

Interpreting and Improving Adversarial Robustness of Deep Neural Networks With Neuron Sensitivity

IEEE Transactions on Image Processing, 2021
Chongzhi Zhang, Aishan Liu, Xianglong Liu   +2 more
exaly