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Found 6 papers, 4 papers with code
Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs.
RobArch: Designing Robust Architectures against Adversarial Attacks
Adversarial Training is the most effective approach for improving the robustness of Deep Neural Networks (DNNs).
Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models
Finally, we find that data augmentation degrades the performance of existing attacks to a larger extent, and we propose an adaptive attack using augmentation to train shadow and attack models that improve attack performance.
Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples
Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples.
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
Although deep neural networks (DNNs) have achieved great success in many tasks, they can often be fooled by \emph{adversarial examples} that are generated by adding small but purposeful distortions to natural examples.
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
By identifying and removing unnecessary features in a DNN model, DeepCloak limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs.
