Results 11 to 20 of about 5,739,313 (302)
Quantum adversarial machine learning [PDF]
Physical Review Research, 2020 Adversarial machine learning is an emerging field that focuses on studying vulnerabilities of machine learning approaches in adversarial settings and developing techniques accordingly to make learning robust to adversarial manipulations. It plays a vital
Sirui Lu, Lu-Ming Duan, Dong-Ling Deng
doaj +4 more sources
Quantum generative adversarial learning [PDF]
Physical Review Letters, 2018 Generative adversarial networks (GANs) represent a powerful tool for classical machine learning: a generator tries to create statistics for data that mimics those of a true data set, while a discriminator tries to discriminate between the true and fake ...
Lloyd, Seth, Weedbrook, Christian
core +6 more sources
Adversarial Machine Learning [PDF]
, 2022 Recent innovations in machine learning enjoy a remarkable rate of adoption across a broad spectrum of applications, including cyber-security. While previous chapters study the application of machine learning solutions to cyber-security, in this chapter we present adversarial machine learning: a field of study concerned with the security of machine ...
Hernández-Castro, C.J. +4 more
+5 more sources
Adversarial Machine Learning [PDF]
IEEE Internet Computing, 2011 The author briefly introduces the emerging field of adversarial machine learning, in which opponents can cause traditional machine learning algorithms to behave poorly in security applications. He gives a high-level overview and mentions several types of attacks, as well as several types of defenses, and theoretical limits derived from a study of near ...
Ling Huang +4 more
+4 more sources
Adversarial Machine Learning - Industry Perspectives [PDF]
SSRN Electronic Journal, 2020 Minor Typos corrected 7 pages, 1 ...
Kumar, Ram Shankar Siva +7 more
openaire +3 more sources
Quantum Adversarial Transfer Learning
Entropy, 2023 Adversarial transfer learning is a machine learning method that employs an adversarial training process to learn the datasets of different domains. Recently, this method has attracted attention because it can efficiently decouple the requirements of tasks from insufficient target data.
Longhan Wang, Yifan Sun, Xiangdong Zhang
openaire +3 more sources
Semantic Adversarial Deep Learning [PDF]
IEEE Design & Test, 2018 Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection.
Sanjit A. Seshia +2 more
openaire +6 more sources
The Limitations of Deep Learning in Adversarial Settings [PDF]
European Symposium on Security and Privacy, 2015 Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks.
Nicolas Papernot +5 more
semanticscholar +1 more source
Mitigating Unwanted Biases with Adversarial Learning [PDF]
AAAI/ACM Conference on AI, Ethics, and Society, 2018 Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases.
B. Zhang +2 more
semanticscholar +1 more source
Targeted Universal Adversarial Examples for Remote Sensing
Remote Sensing, 2022 Researchers are focusing on the vulnerabilities of deep learning models for remote sensing; various attack methods have been proposed, including universal adversarial examples.
Tao Bai, Hao Wang, Bihan Wen
doaj +1 more source