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Found 4 papers, 1 papers with code
Distilling Adversarial Robustness Using Heterogeneous Teachers
Achieving resiliency against adversarial attacks is necessary prior to deploying neural network classifiers in domains where misclassification incurs substantial costs, e. g., self-driving cars or medical imaging.
Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning
First, how can the low transferability between defenses be utilized in a game theoretic framework to improve the robustness?
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
First, we show that successful white-box adversarial attacks on SNNs are highly dependent on the underlying surrogate gradient technique, even in the case of adversarially trained SNNs.
Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks
In this paper, we seek to help alleviate this problem by systematizing the recent advances in adversarial machine learning black-box attacks since 2019.
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