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Found 7 papers, 2 papers with code
Detection and Defense of Unlearnable Examples
Detectability of unlearnable examples with simple networks motivates us to design a novel defense method.
Restore Translation Using Equivariant Neural Networks
Invariance to spatial transformations such as translations and rotations is a desirable property and a basic design principle for classification neural networks.
Achieve Optimal Adversarial Accuracy for Adversarial Deep Learning using Stackelberg Game
Game theory has been used to answer some of the basic questions about adversarial deep learning such as the existence of a classifier with optimal robustness and the existence of optimal adversarial samples for a given class of classifiers.
Adversarial Parameter Attack on Deep Neural Networks
In this paper, a new parameter perturbation attack on DNNs, called adversarial parameter attack, is proposed, in which small perturbations to the parameters of the DNN are made such that the accuracy of the attacked DNN does not decrease much, but its robustness becomes much lower.
Robust and Information-theoretically Safe Bias Classifier against Adversarial Attacks
The work is motivated by the fact that the bias part is a piecewise constant function with zero gradient and hence cannot be directly attacked by gradient-based methods to generate adversaries, such as FGSM.
A Robust Classification-autoencoder to Defend Outliers and Adversaries
In this paper, a robust classification-autoencoder (CAE) is proposed, which has strong ability to recognize outliers and defend adversaries.
Improve the Robustness and Accuracy of Deep Neural Network with $L_{2,\infty}$ Normalization
A lower bound for the robustness measure is given in terms of the $L_{2,\infty}$ norm.
