Results 61 to 70 of about 804,777 (293)
Towards Adversarial Robustness for Multi-Mode Data through Metric Learning
Sensors, 2023 Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial ...
Sarwar Khan +3 more
doaj +1 more source
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
core
CONTRIBUTION ANALYSIS OF THE STATE DEFENSE AWARENESS PROGRAMME TOWARDS THE NATIONAL CHARACTER BUILDING [PDF]
, 2019 Globalization always lead to various kinds of influences, both negative nor positive, on the national character. In Indonesia, globalization has caused a significant degradation of national character which ended by the poor national character of the ...
Adianto, Tri +3 more
core +1 more source
Ovarian Cancer Diagnosis and Chemoresistance Prediction Model Based on cfRNA Molecular Signature
Advanced Science, EarlyView.A deep learning model analyzes cfRNA profiles extracted from the blood of OVCA patients. This innovative approach distinguishes OVCA from healthy controls with high accuracy. Crucially, it reliably predicts patient response to chemotherapy (sensitive versus resistant subgroups).
Qinhao Guo +14 more
wiley +1 more source
Robust Adversarial Defense by Tensor Factorization [PDF]
, 2023 As machine learning techniques become increasingly prevalent in data analysis, the threat of adversarial attacks has surged, necessitating robust defense mechanisms.
Alexandrov, Boian +5 more
core +1 more source
On Procedural Adversarial Noise Attack And Defense
CoRR, 2021 Deep Neural Networks (DNNs) are vulnerable to adversarial examples which would inveigle neural networks to make prediction errors with small perturbations on the input images. Researchers have been devoted to promoting the research on the universal adversarial perturbations (UAPs) which are gradient-free and have little prior knowledge on data ...
Jun Yan 0013 +3 more
openaire +3 more sources
Predicting Performance of Hall Effect Ion Source Using Machine Learning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
Survey on adversarial attacks and defenses for object detection
Tongxin xuebao, 2023 In response to recent developments in adversarial attacks and defenses for object detection, relevant terms and concepts associated with object detection and adversarial learning were first introduced.Subsequently, according to the evolution process of ...
Xinxin WANG +6 more
doaj +2 more sources
Adversarial Attack and Defense on Deep Neural Network-Based Voice Processing Systems: An Overview
Applied Sciences, 2021 Voice Processing Systems (VPSes), now widely deployed, have become deeply involved in people’s daily lives, helping drive the car, unlock the smartphone, make online purchases, etc.
Xiaojiao Chen, Sheng Li, Hao Huang
doaj +1 more source
Sparsity based defense against adversarial examples: v1.0
, 2020 Sparsity-based defense against adversarial attacks on machine learning classifiers. Contains code for the following papers: S. Gopalakrishnan, Z. Marzi, U. Madhow, R.
Soorya Gopalakrishnan
core +1 more source