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Found 9 papers, 1 papers with code
The Space of Adversarial Strategies
From our evaluation we find that attack performance to be highly contextual: the domain, model robustness, and threat model can have a profound influence on attack efficacy.
Adversarial Plannning
We evaluate the performance of the algorithms against two dominant planning algorithms used in commercial applications (D* Lite and Fast Downward) and show both are vulnerable to extremely limited adversarial action.
A Machine Learning and Computer Vision Approach to Geomagnetic Storm Forecasting
They result from high sun activity, which are induced from cool areas on the Sun known as sunspots.
HoneyModels: Machine Learning Honeypots
To harden these systems the ever-growing field of Adversarial Machine Learning has proposed new attack and defense mechanisms.
Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations
With four and eight detector arrays, we collect counts of gamma-rays as features for a suite of machine learning models to localize radioactive material.
On the Robustness of Domain Constraints
Machine learning is vulnerable to adversarial examples-inputs designed to cause models to perform poorly.
Adversarial Examples in Constrained Domains
To assess how these algorithms perform, we evaluate them in constrained (e. g., network intrusion detection) and unconstrained (e. g., image recognition) domains.
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
Detection under Privileged Information
In this paper, we consider an alternate learning approach that trains models using "privileged" information--features available at training time but not at runtime--to improve the accuracy and resilience of detection systems.
