Adversarial machine learning manipulates datasets to mislead machine learning algorithm decisions. We propose a new approach able to detect adversarial attacks, based on eXplainable and Reliable AI. The results obtained show how canonical algorithms may have difficulty in identifying attacks, while the proposed approach is able to correctly identify different adversarial settings
Thesis (Ph.D.)--University of Washington, 2019Deep neural networks have achieved remarkable success ...
Adversarial Machine learning is a field of research lying at the intersection of Machine Learning an...
Machine learning is widely used for detecting and classifying malware. Unfortunately, machine learni...
Pattern recognition systems based on machine learning techniques are nowadays widely used in many di...
Deep learning plays an important role in various disciplines, such as auto-driving, information tech...
Adversarial examples are inputs to a machine learning system that result in an incorrect output from...
Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autono...
Over the last decade, machine learning systems have achieved state-of-the-art performance in many fi...
Machine learning is a subset of Artificial Intelligence which is utilised in a variety of different ...
Cyber security is used to protect and safeguard computers and various networks from ill-intended dig...
Machine learning has become a prevalent tool in many computing applications and modern enterprise sy...
Machine learning is used in myriad aspects, both in academic research and in everyday life, includin...
It has been recently shown that it is possible to cheat many machine learning algorithms -- i.e., ...
There has been an ongoing cycle between stronger attacks and stronger defenses in the adversarial ma...
Over the last decade, adversarial attack algorithms have revealed instabilities in deep learning too...
Thesis (Ph.D.)--University of Washington, 2019Deep neural networks have achieved remarkable success ...
Adversarial Machine learning is a field of research lying at the intersection of Machine Learning an...
Machine learning is widely used for detecting and classifying malware. Unfortunately, machine learni...
Pattern recognition systems based on machine learning techniques are nowadays widely used in many di...
Deep learning plays an important role in various disciplines, such as auto-driving, information tech...
Adversarial examples are inputs to a machine learning system that result in an incorrect output from...
Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autono...
Over the last decade, machine learning systems have achieved state-of-the-art performance in many fi...
Machine learning is a subset of Artificial Intelligence which is utilised in a variety of different ...
Cyber security is used to protect and safeguard computers and various networks from ill-intended dig...
Machine learning has become a prevalent tool in many computing applications and modern enterprise sy...
Machine learning is used in myriad aspects, both in academic research and in everyday life, includin...
It has been recently shown that it is possible to cheat many machine learning algorithms -- i.e., ...
There has been an ongoing cycle between stronger attacks and stronger defenses in the adversarial ma...
Over the last decade, adversarial attack algorithms have revealed instabilities in deep learning too...
Thesis (Ph.D.)--University of Washington, 2019Deep neural networks have achieved remarkable success ...
Adversarial Machine learning is a field of research lying at the intersection of Machine Learning an...
Machine learning is widely used for detecting and classifying malware. Unfortunately, machine learni...