Adversarial machine learning in e-health: Attacking a smart prescription system
Abstract Machine learning (ML) algorithms are the basis of many services we rely on in our
everyday life. For this reason, a new research line has recently emerged with the aim of …
everyday life. For this reason, a new research line has recently emerged with the aim of …
Design and analysis of adversarial samples in safety–critical environment: Disease prediction system
Deep learning is a part of machine learning applied in many applications, from object
detection to disease prediction. In 2013, deep neural networks were vulnerable to perturbed …
detection to disease prediction. In 2013, deep neural networks were vulnerable to perturbed …
Adversarial attacks to machine learning-based smart healthcare systems
AKM Iqtidar Newaz, N Imtiazul Haque… - arXiv e …, 2020 - ui.adsabs.harvard.edu
The increasing availability of healthcare data requires accurate analysis of disease
diagnosis, progression, and realtime monitoring to provide improved treatments to the …
diagnosis, progression, and realtime monitoring to provide improved treatments to the …
Adversarial attacks to machine learning-based smart healthcare systems
The increasing availability of healthcare data requires accurate analysis of disease
diagnosis, progression, and real-time monitoring to provide improved treatments to the …
diagnosis, progression, and real-time monitoring to provide improved treatments to the …
Explaining Vulnerability of Machine Learning to Adversarial Attacks
M Melis - 2021 - iris.unica.it
Pattern recognition systems based on machine learning techniques are nowadays widely
used in many different fields, ranging from biometrics to computer security. In spite of the …
used in many different fields, ranging from biometrics to computer security. In spite of the …
Recurring Threats to Smart Healthcare Systems Based on Machine Learning
Accurate analysis of illness diagnosis, progression, and real-time monitoring, made possible
by the ever-increasing availability of healthcare data, is essential for providing better …
by the ever-increasing availability of healthcare data, is essential for providing better …
Machine learning integrity and privacy in adversarial environments
A Oprea - Proceedings of the 26th ACM Symposium on Access …, 2021 - dl.acm.org
Machine learning is increasingly being used for automated decisions in applications such as
health care, finance, autonomous vehicles, and personalized recommendations. These …
health care, finance, autonomous vehicles, and personalized recommendations. These …
Threat is in the air: Machine learning for wireless network applications
With the spread of wireless application, huge amount of data is generated every day. Thanks
to its elasticity, machine learning is becoming a fundamental brick in this field, and many of …
to its elasticity, machine learning is becoming a fundamental brick in this field, and many of …
Adversarial attacks against medical deep learning systems
The discovery of adversarial examples has raised concerns about the practical deployment
of deep learning systems. In this paper, we demonstrate that adversarial examples are …
of deep learning systems. In this paper, we demonstrate that adversarial examples are …
Toward an understanding of adversarial examples in clinical trials
K Papangelou, K Sechidis, J Weatherall… - Machine Learning and …, 2019 - Springer
Deep learning systems can be fooled by small, worst-case perturbations of their inputs,
known as adversarial examples. This has been almost exclusively studied in supervised …
known as adversarial examples. This has been almost exclusively studied in supervised …