Results 261 to 270 of about 160,235 (299)

Infrared and visible image fusion using GAN with fuzzy logic and Harris Hawks optimization. [PDF]

Sci Rep
Zarimeidani M, Amirabadi A, Amiri N, Ahanian I, Es'haghi S.   +4 more
europepmc   +1 more source

A multi-modal deep learning framework with GAN-based fusion for enhanced landslide detection. [PDF]

PLoS One
Srivats R, Johnson DR, Logeswari G, Saimirra R, Siddiqui M, Sharma A.   +5 more
europepmc   +1 more source

Adversarial Machine Learning: Methods for Attacks and Defenses

Van, Minh Hao
openalex   +1 more source

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 0001, Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, J. D. Tygar   +4 more
exaly   +4 more sources

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Adversarial Machine Learning:

2023
This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stage of attack, attacker goals and objectives, and attacker capabilities and ...
Alina Oprea, Apostol Vassilev
  +4 more sources

Adversarial Machine Learning for Text

Proceedings of the Sixth International Workshop on Security and Privacy Analytics, 2020
In this tutorial, we investigate the history, evolution and latest research topics in the area of adversarial machine learning for text data. Both classical attacks on spam filters and more recent attacks on deep learning models for text classification problems will be discussed. We then discuss proposed and potential defenses against these attacks. We
Daniel Lee, Rakesh M. Verma
openaire   +1 more source

Machine Learning in Adversarial Settings

IEEE Security and Privacy, 2016
Recent advances in machine learning have led to innovative applications and services that use computational structures to reason about complex phenomenon. Over the past several years, the security and machine-learning communities have developed novel techniques for constructing adversarial samples--malicious inputs crafted to mislead (and therefore ...
Patrick McDaniel, Nicolas Papernot, Z Berkay Çelik   +2 more
exaly   +2 more sources

Machine learning in adversarial environments

Machine Learning, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pavel Laskov, Richard Lippmann
openaire   +2 more sources