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Found 6 papers, 1 papers with code
Less is More: The Influence of Pruning on the Explainability of CNNs
To do so, we conducted a pre-study and two human-grounded experiments, assessing the effects of different pruning ratios on CNN explainability.
On the Effect of Adversarial Training Against Invariance-based Adversarial Examples
Another type of adversarial examples are invariance-based adversarial examples, where the images are semantically modified such that the predicted class of the model does not change, but the class that is determined by humans does.
HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models
Fully homomorphic encryption (FHE) is a promising technique to enable individuals using ML services without giving up privacy and protecting the ML model of service providers at the same time.
Pruning in the Face of Adversaries
Available research on the impact of neural network pruning on the adversarial robustness is fragmentary and often does not adhere to established principles of robustness evaluation.
When Should You Defend Your Classifier -- A Game-theoretical Analysis of Countermeasures against Adversarial Examples
Adversarial machine learning, i. e., increasing the robustness of machine learning algorithms against so-called adversarial examples, is now an established field.
Machine Unlearning: Linear Filtration for Logit-based Classifiers
Recently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used, and in particular a "right to be forgotten".
