Results 61 to 70 of about 7,737 (292)

Adversarial Recommender Systems: Attack, Defense, and Advances

, 2022
Adversarial machine learning is the research field investigating vulnerabilities inherent to machine learning systems’ design and ways to defend against them.
Di Noia, Tommaso, Deldjoo, Yashar, Merra, Felice Antonio, Anelli, Vito Walter   +3 more
core   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 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, Jing CHEN, Kun HE, Zijun ZHANG, Ruiying DU, Qiao LI, Jisi SHE   +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

Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong

CoRR, 2017
Ongoing research has proposed several methods to defend neural networks against adversarial examples, many of which researchers have shown to be ineffective. We ask whether a strong defense can be created by combining multiple (possibly weak) defenses. To answer this question, we study three defenses that follow this approach. Two of these are recently
Warren He, James Wei, Xinyun Chen, Nicholas Carlini, Dawn Song   +4 more
openaire   +3 more sources

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

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, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination

Complex & Intelligent Systems
Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model
Xinlei Liu, Jichao Xie, Tao Hu, Peng Yi, Yuxiang Hu, Shumin Huo, Zhen Zhang   +6 more
doaj   +1 more source

You Can’t Fool All the Models: Detect Adversarial Samples via Pruning Models

IEEE Access, 2021
Many adversarial attack methods have investigated the security issue of deep learning models. Previous works on detecting adversarial samples show superior in accuracy but consume too much memory and computing resources.
Renxuan Wang, Zuohui Chen, Hui Dong, Qi Xuan   +3 more
doaj   +1 more source

Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN

CoRR, 2017
We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image.
Hyeungill Lee, Sungyeob Han, Jungwoo Lee 0001   +2 more
openaire   +2 more sources