The maximum mean discrepancy (MMD) test, as a representative two-sample test, could in
principle detect any distributional discrepancy between two datasets. However, it has been …

The maximum mean discrepancy (MMD) test could in principle detect any distributional
discrepancy between two datasets. However, it has been shown that the MMD test is …

Adversarial detection aims to determine whether a given sample is an adversarial one
based on the discrepancy between natural and adversarial distributions. Unfortunately …

Many deep learning models are vulnerable to the adversarial attack, ie, imperceptible but
intentionally-designed perturbations to the input can cause incorrect output of the networks …

Deep neural networks (DNNs) are notorious for their vulnerability to adversarial attacks,
which are small perturbations added to their input images to mislead their prediction …

Deep learning classifiers are known to be vulnerable to adversarial examples. A recent
paper presented at ICML 2019 proposed a statistical test detection method based on the …

Adversarial attacks insert small, imperceptible perturbations to input samples that cause
large, undesired changes to the output of deep learning models. Despite extensive research …

Recently, it has been shown that deep neural networks (DNN) are subject to attacks through
adversarial samples. Adversarial samples are often crafted through adversarial perturbation …

Recent work has shown that state-of-the-art models are highly vulnerable to adversarial
perturbations of the input. We propose cowboy, an approach to detecting and defending …

Models produced by machine learning, particularly deep neural networks, are state-of-the-
art for many machine learning tasks and demonstrate very high prediction accuracy …