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Found 2 papers, 1 papers with code
A Mask-Based Adversarial Defense Scheme
Adversarial attacks hamper the functionality and accuracy of Deep Neural Networks (DNNs) by meddling with subtle perturbations to their inputs. In this work, we propose a new Mask-based Adversarial Defense scheme (MAD) for DNNs to mitigate the negative effect from adversarial attacks.
A Uniform Framework for Anomaly Detection in Deep Neural Networks
Deep neural networks (DNN) can achieve high performance when applied to In-Distribution (ID) data which come from the same distribution as the training set.
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