Assessing Neural Network Robustness via Adversarial Pivotal Tuning. (arXiv:2211.09782v1 [cs.CV])

The ability to assess the robustness of image classifiers to a diverse set of
manipulations is essential to their deployment in the real world. Recently,
semantic manipulations of real images have been considered for this purpose, as
they may not arise using standard adversarial settings. However, such semantic
manipulations are often limited to style, color or attribute changes. While
expressive, these manipulations do not consider the full capacity of a
pretrained generator to affect adversarial image manipulations. In this work, …

adversarial network neural network robustness