Results 121 to 130 of about 7,737 (292)
Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks
Backdoor attacks and adversarial attacks are two major security threats to deep neural networks (DNNs), with the former one is a training-time data poisoning attack that aims to implant backdoor triggers into models by injecting trigger patterns into ...
Wang, Weijian +4 more
core +1 more source
ABSTRACT Understanding innovation policy is pivotal to enhancing national competitiveness for global value chain. This research investigates an SME‐Inclusive public‐private AI innovation network over time in the U.S. context (1991–2023), revealing how a government's demand‐based innovation policy bolsters technological prowess through isomorphism and ...
Jiyoon An
wiley +1 more source
Adversarial training and deep k-nearest neighbors improves adversarial defense of glaucoma severity detection. [PDF]
Riza Rizky LM, Suyanto S.
europepmc +1 more source
Factors influencing the nature of client complaint behaviour in the aftermath of adverse events
Abstract Background Negative veterinary client complaint behaviour poses wellbeing and reputational risks. Adverse events are one source of complaint. Identifying factors that influence adverse event‐related complaint behaviour is key to mitigating detrimental consequences and harnessing information that can be used to improve service quality, patient ...
Julie Gibson +3 more
wiley +1 more source
Time-Constrained Adversarial Defense in IoT Edge Devices through Kernel Tensor Decomposition and Multi-DNN Scheduling. [PDF]
Kim M, Joo S.
europepmc +1 more source
Moving Target Defense: Defending against Adversarial Defense
: A defense-by-randomization framework is proposed as an effective defense mechanism against different types of adversarial attacks on neural networks. Experiments were conducted by selecting a combination of differently constructed image classification ...
core
ABSTRACT Objective To provide an overview of potential biases resulting from the utilization of artificial intelligence (AI) in otolaryngology and techniques to mitigate them. Data Sources Literature review and expert opinion. Conclusions AI promises to fundamentally transform medicine.
Matthew T. Ryan, David A. Gudis
wiley +1 more source
Research on adversarial attack and defense of photovoltaic power prediction
Deep neural networks have been widely used in photovoltaic power prediction, but they are vulnerable to adversarial attacks. In order to improve the robustness of the prediction model, an adversarial attack algorithm based on fast gradient sign method ...
Zhou Wang
doaj +1 more source
Credit‐Driven Adaptive Grouping for Refined Cooperative Multi‐Agent Reinforcement Learning
ABSTRACT Policy heterogeneity is crucial for achieving sophisticated coordination in complex collaborative tasks, which has emerged as one of the key challenges in multi‐agent reinforcement learning (MARL) in recent years. Notably, the grouping paradigm has made remarkable progress in addressing policy heterogeneity.
Yirui Liu +6 more
wiley +1 more source
Image super-resolution as a defense against adversarial attacks
Convolutional Neural Networks have achieved significant success across multiple computer vision tasks. However, they are vulnerable to carefully crafted, human-imperceptible adversarial noise patterns which constrain their deployment in critical security-
Mustafa, Aamir +4 more
core +1 more source

