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Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness
The deep neural network has attained significant efficiency in image recognition.
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks
Adversarial attacks are considered the intrinsic vulnerability of CNNs.
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework
Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks (DNNs).
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness
A new constraints norm is proposed in model training based on these criteria to isolate adversarial transferability without any prior knowledge of adversarial samples.
Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout
In this paper, we propose a new gradient iteration framework, which redefines the relationship between the above three.
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model
The defense strategies are particularly passive in these processes, and enhancing initiative of such strategies can be an effective way to get out of this arms race.
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense
In image classification of deep learning, adversarial examples where inputs intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them.
