Skip to search formSkip to main contentSkip to account menu

Adversarial machine learning

Adversarial machine learning is a research field that lies at the intersection of machine learning and computer security. It aims to enable the safe… 
Wikipedia (opens in a new tab)

Related topics

Related topics

6 relations
BiometricsInternational Conference on Machine LearningJournal of Machine Learning ResearchTensorFlow

Broader (2)

Computer securityMachine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2020
2020
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
Machine learning models have been successfully applied to a wide range of applications including computer vision, natural… 
2020
2020
Detecting, Diagnosing, Deflecting and Designing Adversarial Attacks
Author(s): Qin, Yao | Advisor(s): Cottrell, Garrison | Abstract: There has been an ongoing cycle between stronger attacks and… 
2019
2019
Adversarial Attacks on Grid Events Classification: An Adversarial Machine Learning Approach
With the ever-increasing reliance on data for data-driven applications in power grids, such as event cause analysis, the… 
2019
2019
Legislating Autonomous Vehicles against the Backdrop of Adversarial Machine Learning Findings
Recent studies on adversarial machine learning1 made Michael Grossman, a Texas-based injury lawyer, skeptical of the viability of… 
2019
2019
Adversarial Machine Learning with Double Oracle
We aim to improve the general adversarial machine learning solution by introducing the double oracle idea from game theory, which… 
2019
2019
Defending Against Adversarial Machine Learning
An Adversarial System to attack and an Authorship Attribution System (AAS) to defend itself against the attacks are analyzed… 
2018
2018
Towards Adversarial Configurations for Software Product Lines
Ensuring that all supposedly valid configurations of a software product line (SPL) lead to well-formed and acceptable products is… 
2018
2018
Intrusion-Resilient Classifier Approximation: From Wildcard Matching to Range Membership
We study the problem of securing machine learning classifiers against intrusion attacks (i.e., attacks that somehow retrieve the… 
2016
2016
Security Analytics in the Context of Adversarial Machine Learning
Bio Doug Tygar is Professor of Computer Science at UC Berkeley and also a Professor of Information Management at UC Berkeley. He… 
2014
2014
Developing technology-assisted multi-disciplinary learning strategies
The construction industry is multi-disciplinary and collaborative in nature. Project managers are expected to understand the… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)