Results 251 to 260 of about 237,731 (274)

Advops: Decoupling Adversarial Examples

Pattern Recognition, 2023
Donghua Wang, Wen Yao, Tingsong Jiang, Xiaoqian Chen   +3 more
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Rethinking Adversarial Examples

Traditionally, adversarial examples have been defined as imperceptible perturbations that fool deep neural networks. This thesis challenges this view by examining unrestricted adversarial examples – a broader class of manipulations that can compromise model security while preserving semantics.
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Adversarial examples: A survey

2018 Baltic URSI Symposium (URSI), 2018
Adversarial examples are a phenomenon that have gathered a lot of attention in recent studies. The fact that the addition of very small, but carefully crafted perturbations to the inputs of sophisticated and high performing machine learning models may cause them to make significant errors, is both fascinating and important.
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Adversarial Machine Learning in Wireless Communications Using RF Data: A Review

IEEE Communications Surveys and Tutorials, 2023
Damilola Adesina, Yalin E Sagduyu, Lijun Qian   +2 more
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Generative Adversarial Networks (GANs)

ACM Computing Surveys, 2022
Divya Saxena, Jiannong Cao
exaly  

Generative Adversarial Networks

ACM Computing Surveys, 2022
Zhipeng Cai, Honghui Xu, Yi Pan
exaly  

A Survey on Generative Adversarial Networks: Variants, Applications, and Training

ACM Computing Surveys, 2022
Songyuan Li
exaly  

Generative Adversarial Networks in Time Series: A Systematic Literature Review

ACM Computing Surveys, 2023
Eoin Brophy, Zhengwei Wang, Qi She
exaly  

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

ACM Computing Surveys, 2022
Ishai Rosenberg, Asaf Shabtai
exaly  

Generative Adversarial Networks in Computer Vision

ACM Computing Surveys, 2022
Zhengwei Wang
exaly