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Found 6 papers, 3 papers with code
Improving Adversarial Transferability via Model Alignment
During the alignment process, the parameters of the source model are fine-tuned to minimize an alignment loss.
A Survey on Transferability of Adversarial Examples across Deep Neural Networks
This survey explores the landscape of the adversarial transferability of adversarial examples.
Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods
In the context of deep learning, our experiments show that SGD-trained neural networks have smaller Lipschitz constants, explaining the better robustness to input perturbations than those trained with adaptive gradient methods.
SAGE: Saliency-Guided Mixup with Optimal Rearrangements
For image classification, the most popular data augmentation techniques range from simple photometric and geometrical transformations, to more complex methods that use visual saliency to craft new training examples.
Improving Hierarchical Adversarial Robustness of Deep Neural Networks
Do all adversarial examples have the same consequences?
SOAR: Second-Order Adversarial Regularization
Adversarial training is a common approach to improving the robustness of deep neural networks against adversarial examples.
