Results 31 to 40 of about 33,606 (255)
Adversarial robustness in deep learning: attacks on fragile neurons [PDF]
, 2021 We identify fragile and robust neurons of deep learning architectures using nodal dropouts of the first convolutional layer. Using an adversarial targeting algorithm, we correlate these neurons with the distribution of adversarial attacks on the network.
Martino, Ivan, +11 more
core +1 more source
eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics
IEEE Access, 2022 Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and
Ivan Vaccari +4 more
doaj +1 more source
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning
, 2021 Despite the rapid increase of data available to train machine-learning algorithms in many domains, several applications suffer from a paucity of representative and diverse data.
Braren, Rickmer +7 more
core +1 more source
Attacking neural machine translations via hybrid attention learning [PDF]
, 2023 Deep-learning based natural language processing (NLP) models are proven vulnerable to adversarial attacks. However, there is currently insufficient research that studies attacks to neural machine translations (NMTs) and examines the robustness of deep ...
Ni, M, Liu, W, Zhu, T, Wang, C, Yu, S
core +1 more source
Adversarial machine learning beyond the image domain [PDF]
, 2019 Machine learning systems have had enormous success in a wide range of fields from computer vision, natural language processing, and anomaly detection.
Jones, Kevin +7 more
core +1 more source
Advanced Materials, EarlyView.This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley +1 more source
NUST Journal of Engineering Sciences, 2022 The availability of information and its integrity and confidentiality are important factors in information and communication of the system security. The DDoS attack generally means Distributed denial of services generates many enormous packets to slow ...
Zahid Iqbal +3 more
doaj +1 more source
On the Generalization Analysis of Adversarial Learning
, 2022 Many recent studies have highlighted the susceptibility of virtually all machine-learning models to adversarial attacks. Adversarial attacks are imperceptible changes to an input example of a given prediction model. Such changes are carefully designed to
Mustafa, Waleed +2 more
core
All‐Optical Reconfigurable Physical Unclonable Function for Sustainable Security
Advanced Materials, EarlyView.An all‐optical reconfigurable physical unclonable function (PUF) is demonstrated using plasmonic coupling–induced sintering of optically trapped gold nanoparticles, where Brownian motion serves as a robust entropy source. The resulting optical PUF exhibits high encoding density, strong resistance to modeling attacks, and practical authentication ...
Jang‐Kyun Kwak +4 more
wiley +1 more source
Breaking Machine Learning Models with Adversarial Attacks and its Variants
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceMachine learning models can be by adversarial attacks, subtle, imperceptible perturbations to inputs that cause the model to produce erroneous outputs.
Pavan Reddy
doaj +1 more source