Advances in adversarial attacks and defenses in computer vision: A survey
Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …
ability to accurately solve complex problems is employed in vision research to learn deep …
{X-Adv}: Physical adversarial object attacks against x-ray prohibited item detection
Adversarial attacks are valuable for evaluating the robustness of deep learning models.
Existing attacks are primarily conducted on the visible light spectrum (eg, pixel-wise texture …
Existing attacks are primarily conducted on the visible light spectrum (eg, pixel-wise texture …
Resilience and resilient systems of artificial intelligence: taxonomy, models and methods
Artificial intelligence systems are increasingly being used in industrial applications, security
and military contexts, disaster response complexes, policing and justice practices, finance …
and military contexts, disaster response complexes, policing and justice practices, finance …
Threat of adversarial attacks on deep learning in computer vision: A survey
Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …
computer vision, it has become the workhorse for applications ranging from self-driving cars …
A survey on learning to reject
Learning to reject is a special kind of self-awareness (the ability to know what you do not
know), which is an essential factor for humans to become smarter. Although machine …
know), which is an essential factor for humans to become smarter. Although machine …
Detecting adversarial data by probing multiple perturbations using expected perturbation score
Adversarial detection aims to determine whether a given sample is an adversarial one
based on the discrepancy between natural and adversarial distributions. Unfortunately …
based on the discrepancy between natural and adversarial distributions. Unfortunately …
A new context-aware framework for defending against adversarial attacks in hyperspectral image classification
Deep neural networks play a significant role in hyperspectral image (HSI) processing, yet
they can be easily fooled when trained with adversarial samples (generated by adding tiny …
they can be easily fooled when trained with adversarial samples (generated by adding tiny …
Similarity-based integrity protection for deep learning systems
Deep learning technologies have achieved remarkable success in various tasks, ranging
from computer vision, object detection to natural language processing. Unfortunately, state …
from computer vision, object detection to natural language processing. Unfortunately, state …
Towards intrinsic adversarial robustness through probabilistic training
Modern deep neural networks have made numerous breakthroughs in real-world
applications, yet they remain vulnerable to some imperceptible adversarial perturbations …
applications, yet they remain vulnerable to some imperceptible adversarial perturbations …
Defenses in adversarial machine learning: A survey
Adversarial phenomenon has been widely observed in machine learning (ML) systems,
especially in those using deep neural networks, describing that ML systems may produce …
especially in those using deep neural networks, describing that ML systems may produce …