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Found 4 papers, 0 papers with code
Generating Semantic Adversarial Examples with Differentiable Rendering
In this paper we explore semantic adversarial examples (SAEs) where an attacker creates perturbations in the semantic space representing the environment that produces input for the ML model.
On the Need for Topology-Aware Generative Models for Manifold-Based Defenses
These defenses rely on the assumption that data lie in a manifold of a lower dimension than the input space.
The Manifold Assumption and Defenses Against Adversarial Perturbations
Interestingly, we find that a recent objective by Madry et al. encourages training a model that satisfies well our formal version of the goodness property, but has a weak control of points that are wrong but with low confidence.
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
In this paper we study leveraging confidence information induced by adversarial training to reinforce adversarial robustness of a given adversarially trained model.
