Results 191 to 200 of about 5,380,268 (331)

Improving the Transferability of Adversarial Examples With a Noise Data Enhancement Framework and Random Erasing. [PDF]

Front Neurorobot, 2021
Xie P, Shi S, Yang S, Qiao K, Liang N, Wang L, Chen J, Hu G, Yan B.   +8 more
europepmc   +1 more source

Adversarial examples in Android malware detection

, 2020
Područje računalne sigurnosti je podložno stalnim promjenama i nadogradnjama. Primjena metoda strojnog učenja kao tehnike očuvanja sigurnosti predstavlja pozitivnu promjenu i unaprjeđuje postojeće metode obrane. U radu smo se bazirali na dinamičke napade
Kujundžić, Martina
core  

Adversarial Examples in Constrained Domains

, 2022
Machine learning algorithms have been shown to be vulnerable to adversarial manipulation through systematic modification of inputs (e.g., adversarial examples) in domains such as image recognition.
McDaniel, Patrick, Papernot, Nicolas, Weisman, Michael, Sheatsley, Ryan, Verma, Gunjan   +4 more
core  

Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining

Advanced Intelligent Systems, EarlyView.
Calibration‐free electromyography motor intent decoding is enabled through large‐scale supervised pretraining across heterogeneous datasets. A Spatially Aware Feature‐learning Transformer processes variable channel counts and electrode geometries, allowing transfer across users and recording setups. On a held‐out benchmark, fine‐tuned cross‐user models
Alexander E. Olsson, Nebojša Maleševic, Anders Björkman, Christian Antfolk   +3 more
wiley   +1 more source

Adversarial Examples-Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices. [PDF]

IEEE Internet Things J, 2021
Rahman A, Hossain MS, Alrajeh NA, Alsolami F.   +3 more
europepmc   +1 more source

Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy

Advanced Intelligent Systems, EarlyView.
Hallgrimson et al. introduce a machine learning algorithm, siMILe, that takes features of single‐molecule localization microscopy localization clusters (e.g., size and sphericity) and finds the clusters that are associated with certain cell conditions (such as differential protein expression or drug treatment).
Christian Hallgrimson, Y. Lydia Li, Claire A. Shou, Ben Cardoen, John Lim, Timothy H. Wong, Ismail M. Khater, Ivan Robert Nabi, Ghassan Hamarneh   +8 more
wiley   +1 more source

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Advanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal, Jan Kubíček, Jana Bahrová, Alice Varyšová, Martin Augustýnek, Marek Penhaker, Juraj Timkovič   +6 more
wiley   +1 more source

Adversarial examples in neural networks

In recent years, development in various areas such as computer vision and natural language processing, has exposed deep learning technology to security risks gradually.
Lim, Ruihong
core  

An Intelligent Feature Engineering‐Driven Hybrid Framework for Adversarial Domain Name System Tunneling Detection

Advanced Intelligent Systems, EarlyView.
This study presents a novel framework that enhances the reliability of DNS traffic monitoring using a hybrid long short‐term memory‐deep neural network (LSMT‐DNN) architecture, enabling robust detection of adversarial DNS tunneling. The proposed framework leverages feature extraction from DNS traffic patterns, including domain request sequences, query ...
Ahmad Almadhor, Shtwai Alsubai, Monji Mohamed Zaidi, Natalia Kryvinska, Abdullah Al Hejaili, Sidra Abbas   +5 more
wiley   +1 more source

Improving the Transferability of Adversarial Examples via Direction Tuning

, 2023
In the transfer-based adversarial attacks, adversarial examples are only generated by the surrogate models and achieve effective perturbation in the victim models.
Zhang, Hanlin, Yang, Xiangyuan, Lin, Jie, Yang, Xinyu, Zhao, Peng   +4 more
core