Search Results for author:
Found 2 papers, 1 papers with code
Universal Rules for Fooling Deep Neural Networks based Text Classification
Here, we go beyond attacks to investigate, for the first time, universal rules, i. e., rules that are sample agnostic and therefore could turn any text sample in an adversarial one.
One pixel attack for fooling deep neural networks
The results show that 67. 97% of the natural images in Kaggle CIFAR-10 test dataset and 16. 04% of the ImageNet (ILSVRC 2012) test images can be perturbed to at least one target class by modifying just one pixel with 74. 03% and 22. 91% confidence on average.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
