Search Results for author:
Found 4 papers, 3 papers with code
Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction
Machine learning (ML) models are vulnerable to membership inference attacks (MIAs), which determine whether a given input is used for training the target model.
Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks
Therefore, Jujutsu leverages generative adversarial networks (GAN) to perform localized attack recovery by synthesizing the semantic contents of the input that are corrupted by the attacks, and reconstructs a ``clean'' input for correct prediction.
TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications
TensorFI is a configurable FI tool that is flexible, easy to use, and portable.
A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction
The adoption of deep neural networks (DNNs) in safety-critical domains has engendered serious reliability concerns.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
