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Found 3 papers, 1 papers with code
Improving Perceptual Quality of Adversarial Images Using Perceptual Distance Minimization and Normalized Variance Weighting
We show that these methods could also be used in conjunction to improve the perceptual quality of adversarial examples and demonstrate the quantitative improvements on CIFAR-10 and NIPS2017 Adversarial Learning Challenge datasets.
Training Universal Adversarial Perturbations with Alternating Loss Functions
Despite being very successful, deep learning models were shown to be vulnerable to crafted perturbations.
Imperceptible Adversarial Examples by Spatial Chroma-Shift
Deep Neural Networks have been shown to be vulnerable to various kinds of adversarial perturbations.
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