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Found 4 papers, 3 papers with code
Optimal Zero-Shot Detector for Multi-Armed Attacks
We approach this defensive strategy with utmost caution, operating in an environment where the defender possesses significantly less information compared to the attacker.
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Multi-armed adversarial attacks, in which multiple algorithms and objective loss functions are simultaneously used at evaluation time, have been shown to be highly successful in fooling state-of-the-art adversarial examples detectors while requiring no specific side information about the detection mechanism.
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors
Detection of adversarial examples has been a hot topic in the last years due to its importance for safely deploying machine learning algorithms in critical applications.
DOCTOR: A Simple Method for Detecting Misclassification Errors
Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations where DNN are implemented as "black boxes".
