Results 51 to 60 of about 543,217 (204)
Adversarial Attacks against the Perception System of Autonomous Vehicles
, 2023 The rapid advancement in autonomous driving technology underscores the importance of studying the fragility of perception systems in autonomous vehicles, particularly due to their profound impact on public transportation safety.
Gao, Yuxing (author)
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Adversarial Risk Análysis for Counterterrorism Modelling [PDF]
, 2013 Recent large scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the ...
Ríos, Jesús, Ríos Insúa, David
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Probabilistic Categorical Adversarial Attack & Adversarial Training
, 2023 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 ...
Tang, Jiliang +6 more
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Comprehensive comparisons of gradient-based multi-label adversarial attacks
Complex & Intelligent SystemsAdversarial examples which mislead deep neural networks by adding well-crafted perturbations have become a major threat to classification models. Gradient-based white-box attack algorithms have been widely used to generate adversarial examples.
Zhijian Chen +4 more
doaj +1 more source
Adversarial Ranking Attack and Defense [PDF]
, 2020 Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems remains under-explored. In this paper, we propose two attacks against deep ranking systems, i.e., Candidate Attack and Query Attack, that can ...
Mo Zhou +4 more
openaire +3 more sources
Adversarial Attack for Asynchronous Event-Based Data
, 2022 Deep neural networks (DNNs) are vulnerable to adversarial examples that are carefully designed to cause the deep learning model to make mistakes. Adversarial examples of 2D images and 3D point clouds have been extensively studied, but studies on event ...
Myung, Hyun +3 more
core +1 more source
Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems
, 2020 Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine Learning models used in a cloud data ...
Vähäkainu, Petri +2 more
core +1 more source
Timbre-reserved Adversarial Attack in Speaker Identification [PDF]
, 2023 As a type of biometric identification, a speaker identification (SID) system is confronted with various kinds of attacks. The spoofing attacks typically imitate the timbre of the target speakers, while the adversarial attacks confuse the SID system by ...
Guo, Pengcheng +4 more
core +1 more source
Adversarial Attacks to Manipulate Target Localization of Object Detector
IEEE AccessAdversarial attack has gradually become an important branch in the field of artificial intelligence security, where the potential threat brought by adversarial example attack is more not to be ignored.
Kai Xu +7 more
doaj +1 more source
Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
Applied Sciences, 2022 The ever-evolving cybersecurity environment has given rise to sophisticated adversaries who constantly explore new ways to attack cyberinfrastructure. Recently, the use of deep learning-based intrusion detection systems has been on the rise. This rise is
Kudzai Sauka +3 more
doaj +1 more source