Recent work has shown that imperceptible perturbations can be applied to craft unlearnable examples (ULEs), i. e. images whose content cannot be used to improve a classifier during training.
In this paper, we carry out a systematic robustness analysis of ACE from both the attack and defense perspectives by varying the bound of the color filter parameters.
We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification.
The success of image perturbations that are designed to fool image classifier is assessed in terms of both adversarial effect and visual imperceptibility.
An adversarial query is an image that has been modified to disrupt content-based image retrieval (CBIR) while appearing nearly untouched to the human eye.
As deep learning approaches to scene recognition emerge, they have continued to leverage discriminative regions at multiple scales, building on practices established by conventional image classification research.