Results 21 to 30 of about 160,235 (299)

Impact of adversarial examples on deep learning models for biomedical image segmentation [PDF]

, 2019
Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples.
C Pena-Betancor, M Everingham, O Ronneberger, T Heimann   +3 more
core   +4 more sources

Adversarial Attacks and Defenses in Deep Learning

Engineering, 2020
With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms.
Kui Ren, Tianhang Zheng, Zhan Qin, Xue Liu   +3 more
doaj   +1 more source

Research on filter-based adversarial feature selection against evasion attacks

Dianxin kexue, 2023
With the rapid development and widespread application of machine learning technology, its security has attracted increasing attention, leading to a growing interest in adversarial machine learning.In adversarial scenarios, machine learning techniques are
Qimeng HUANG, Miaomiao WU, Yun LI
doaj   +2 more sources

Adversarial Machine Learning:

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 ...
Apostol Vassilev
  +5 more sources

A Robust Network Intrusion Detection System Using Random Forest Based Random Subspace Ensemble to Defend Against Adversarial Attacks

Advances in Electrical and Computer Engineering, 2023
In recent years, machine learning (ML) has had a significant influence on the discipline of computer security. In network security, intrusion detection systems increasingly employ machine learning techniques.
NATHANIEL, D., SOOSAI, A.
doaj   +1 more source

A Distributed Biased Boundary Attack Method in Black-Box Attack

Applied Sciences, 2021
The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios.
Fengtao Xiang, Jiahui Xu, Wanpeng Zhang, Weidong Wang   +3 more
doaj   +1 more source

Adversarial Machine Learning at Scale

CoRR, 2016
17 pages, 5 ...
Alexey Kurakin, Ian J. Goodfellow, Samy Bengio   +2 more
openaire   +3 more sources

Ethical Adversaries [PDF]

ACM SIGKDD Explorations Newsletter, 2021
Machine learning is being integrated into a growing number of critical systems with far-reaching impacts on society. Unexpected behaviour and unfair decision processes are coming under increasing scrutiny due to this widespread use and its theoretical considerations.
Pieter Delobelle, Paul Temple, Gilles Perrouin, Benoît Frénay, Patrick Heymans, Bettina Berendt   +5 more
openaire   +4 more sources

Law and Adversarial Machine Learning

CoRR, 2018
When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion attacks to show how some attacks ...
Ram Shankar Siva Kumar, David R. O'Brien, Kendra Albert, Salome Vilojen   +3 more
openaire   +2 more sources

Development of a Machine-Learning Intrusion Detection System and Testing of Its Performance Using a Generative Adversarial Network

Sensors, 2023
Intrusion detection and prevention are two of the most important issues to solve in network security infrastructure. Intrusion detection systems (IDSs) protect networks by using patterns to detect malicious traffic. As attackers have tried to dissimulate
Andrei-Grigore Mari, Daniel Zinca, Virgil Dobrota   +2 more
doaj   +1 more source