Adversarial training and deep k-nearest neighbors improves adversarial defense of glaucoma severity detection. [PDF]
Heliyon, 2022 Riza Rizky LM, Suyanto S.
europepmc +1 more source
World Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.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
Time-Constrained Adversarial Defense in IoT Edge Devices through Kernel Tensor Decomposition and Multi-DNN Scheduling. [PDF]
Sensors (Basel), 2022 Kim M, Joo S.
europepmc +1 more source
Active Defense Against Voice Conversion Through Generative Adversarial Network [PDF]
Active defense is an important approach to counter speech deepfakes that threaten individuals’ privacy, property, and reputation. However, the existing works in this field suffer from issues such as time-consuming and ordinary defense effectiveness. This
Zhao, Guoying +3 more
core +1 more source
Credit‐Driven Adaptive Grouping for Refined Cooperative Multi‐Agent Reinforcement Learning
CAAI Transactions on Intelligence Technology, EarlyView.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
Enhancing Resilience Against Adversarial Attacks of Deep Neural Networks Using Efficient Two-Step Adversarial Defense [PDF]
, 2019 In recent years, deep neural networks have demonstrated outstanding performance in many machine learning tasks. However, researchers have discovered that these state-of-the-art models are vulnerable to adversarial examples: legitimate examples added by ...
Chang, Ting-Jui
core +1 more source
Research on adversarial attack and defense of photovoltaic power prediction
Dianzi Jishu YingyongDeep 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
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Accurate load forecasting and reliable anomaly detection are critical for the stable operation of modern smart grids (SGs), which increasingly rely on cyber‐connected infrastructures. However, the integration of smart metres and two‐way communication exposes SGs to data integrity attacks that can manipulate consumption measurements, degrade ...
Murad Ali Khan +4 more
wiley +1 more source
Symmetry Defense Against XGBoost Adversarial Perturbation Attacks
, 2023 We examine whether symmetry can be used to defend tree-based ensemble classifiers such as gradient-boosting decision trees (GBDTs) against adversarial perturbation attacks.
Lindqvist, Blerta
core
Robust Multi‐Source Batch Normalisation for Test‐Time Batch Adaptation
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Test‐Time Batch Adaptation (TTBA) aims to adapt a pre‐trained source model to small, unlabelled target batches at test time. The TTBA methods focus on adapting the pre‐trained model or the target data in a one‐to‐one alignment paradigm. However, these one‐to‐one alignment paradigms assume that the source domain may share the same knowledge ...
Xinlin Xiao +3 more
wiley +1 more source