Results 31 to 40 of about 447,373 (304)
Detection of Adversarial Attacks and Characterization of Adversarial Subspace [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Adversarial attacks have always been a serious threat for any data-driven model. In this paper, we explore subspaces of adversarial examples in unitary vector domain, and we propose a novel detector for defending our models trained for environmental sound classification.
Mohammad Esmaeilpour +2 more
openaire +2 more sources
Detection of Physical Adversarial Attacks on Traffic Signs for Autonomous Vehicles [PDF]
, 2023 Current vision-based detection models within Autonomous Vehicles, can be susceptible to changes within the physical environment, which cause unexpected issues. Physical attacks on traffic signs could be malicious or naturally occurring, causing incorrect
Radoglou-Grammatikis, P. +4 more
core +1 more source
Probabilistic Categorical Adversarial Attack & Adversarial Training
CoRR, 2022 The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples with categorical data is an important problem but lack of extensive exploration.
Xu, Han +6 more
openaire +3 more sources
Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks
CoRR, 2021 Accepted to RSEML Workshop at AAAI ...
Marissa Dotter +5 more
openaire +3 more sources
Remote Sensing, 2023 Deep learning (DL) models have recently been widely used in UAV aerial image semantic segmentation tasks and have achieved excellent performance. However, DL models are vulnerable to adversarial examples, which bring significant security risks to safety ...
Zhen Wang +3 more
doaj +1 more source
Query complexity of adversarial attacks
CoRR, 2020 There are two main attack models considered in the adversarial robustness literature: black-box and white-box. We consider these threat models as two ends of a fine-grained spectrum, indexed by the number of queries the adversary can ask. Using this point of view we investigate how many queries the adversary needs to make to design an attack that is ...
Grzegorz Gluch, Rüdiger L. Urbanke
openaire +3 more sources
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks [PDF]
, 2023 While the literature on security attacks and defense of Machine Learning (ML) systems mostly focuses on unrealistic adversarial examples, recent research has raised concern about the under-explored field of realistic adversarial attacks and their ...
Simonetto, Thibault +6 more
core +1 more source
Functional Adversarial Attacks
CoRR, 2019 Accepted to NeurIPS ...
Cassidy Laidlaw, Soheil Feizi
openaire +3 more sources
Robustness of Deep Learning Models for Vision Tasks
Applied Sciences, 2023 In recent years, artificial intelligence technologies in vision tasks have gradually begun to be applied to the physical world, proving they are vulnerable to adversarial attacks.
Youngseok Lee, Jongweon Kim
doaj +1 more source
Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving
Sensors, 2023 Deep learning networks have demonstrated outstanding performance in 2D and 3D vision tasks. However, recent research demonstrated that these networks result in failures when imperceptible perturbations are added to the input known as adversarial attacks.
K. T. Yasas Mahima +3 more
doaj +1 more source