[HTML][HTML] A survey on neural networks for (cyber-) security and (cyber-) security of neural networks
M Pawlicki, R Kozik, M Choraś - Neurocomputing, 2022 - Elsevier
The goal of this systematic and broad survey is to present and discuss the main challenges
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
Act-detector: Adaptive channel transformation-based light-weighted detector for adversarial attacks
With the extensive application of deep neural networks (DNNs) in computer vision tasks, the
vulnerability of such systems to carefully crafted adversarial examples has attracted …
vulnerability of such systems to carefully crafted adversarial examples has attracted …
Robust Machine Learning Using Diversity and Blockchain
Abstract Machine Learning (ML) algorithms are used in several smart city-based
applications. However, ML is vulnerable to adversarial examples that significantly alter its …
applications. However, ML is vulnerable to adversarial examples that significantly alter its …
Exploring the role of input and output layers of a deep neural network in adversarial defense
JN Paranjape, RK Dubey… - … on Computing and Data …, 2020 - ieeexplore.ieee.org
Deep neural networks are learning models having achieved state of the art performance in
many fields like prediction, computer vision, language processing and so on. However, it …
many fields like prediction, computer vision, language processing and so on. However, it …