Results 241 to 250 of about 7,172,817 (283)

On the generalization limits of quantum generative adversarial networks with pure state generators. [PDF]

Sci Rep
Frkatovic J, Malemath A, Kankeu I, Werner Y, Tschöpe M, Rey VF, Suh S, Lukowicz P, Palaiodimopoulos N, Kiefer-Emmanouilidis M.   +9 more
europepmc   +1 more source

NARVGA: A Hybrid Framework Integrating Matrix Factorisation and Adversarial Graph Learning for circRNA-Disease Association Prediction. [PDF]

Biology (Basel)
Lu MS, Wei MM, Liu CC, Wang L, Ruan CW.
europepmc   +1 more source

Lightweight Security-by-Design for TinyML Models in Constrained Embedded Environments. [PDF]

Sensors (Basel)
Alanazi HA, Alshammari KR.
europepmc   +1 more source

Self-defending 6G networks through AI-driven adaptive decoy generation at the edge. [PDF]

Sci Rep
M J RM, G PR, Gopal P, Sathyamoorthy M, Nagane AS, Keshava R.   +5 more
europepmc   +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 ...
J D Tygar
exaly   +5 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 ...
Aneesh Sreevallabh Chivukula, Xinghao Yang, Bo Liu, Wei Liu, Wanlei Zhou   +4 more
  +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   +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

Machine Learning in Adversarial Settings

IEEE Security & 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 D. McDaniel, Nicolas Papernot, Z. Berkay Celik   +2 more
openaire   +2 more sources