3 accepted papers
Deep learning models are vulnerable to privacy attacks due to their tendency to memorize individual training examples. Theoretically-sound defenses such as differential privacy can defend against this threat, but model performance often suffers. Empirical defenses may thwart existing attacks while m…
Data augmentation techniques, such as image transformations and combinations, are highly effective at improving the generalization of computer vision models, especially when training data is limited. However, such techniques are fundamentally incompatible with differentially private learning approac…
The targeted transferability of adversarial samples enables attackers to exploit black-box models in the real-world. The most popular method to produce these adversarial samples is optimization attacks, which have been shown to achieve a high level of transferability in some domains. However, recent…