Comparison of deep neural network fooling methods on the accuracy of classification

The ability to train neural networks depends on access to data. In some areas, for example in medicine, it is difficult to obtain large datasets since medical data can contain very sensitive information. It is desirable to anonymize the dataset in such a way that the utility of machine learning prediction models is preserved. In this paper, we compare different methods of fooling deep neural networks. We investigate how different algorithms affects the accuracy of one classification task while fooling classifier in the other classification task.