Results 61 to 70 of about 1,209,773 (317)

Adversarial attacks on deep learning models in smart grids

Energy Reports, 2022
A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid ...
Jingbo Hao, Yang Tao
doaj   +1 more source

MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

, 2018
Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs.
Chen, Yiran, Cheng, Hsin-Pai, Li, Hai, Li, Sicheng, Song, Chang, Wu, Chunpeng, Wu, Qing, Yang, Huanrui   +7 more
core   +1 more source

Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods [PDF]

AAAI/ACM Conference on AI, Ethics, and Society, 2019
As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner.
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju   +4 more
semanticscholar   +1 more source

Universal Adversarial Training Using Auxiliary Conditional Generative Model-Based Adversarial Attack Generation

Applied Sciences, 2023
While Machine Learning has become the holy grail of modern-day computing, it has many security flaws that have yet to be addressed and resolved. Adversarial attacks are one of these security flaws, in which an attacker appends noise to data samples that ...
Hiskias Dingeto, Juntae Kim
doaj   +1 more source

Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks

, 2021
Accepted to RSEML Workshop at AAAI ...
Dotter, Marissa, Xie, Sherry, Manville, Keith, Harguess, Josh, Busho, Colin, Rodriguez, Mikel   +5 more
openaire   +2 more sources

Adversarial Attacks and Defenses

ACM SIGKDD Explorations Newsletter, 2021
Despite the recent advances in a wide spectrum of applications, machine learning models, especially deep neural networks, have been shown to be vulnerable to adversarial attacks. Attackers add carefully-crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions.
Liu, Ninghao, Du, Mengnan, Guo, Ruocheng, Liu, Huan, Hu, Xia   +4 more
openaire   +2 more sources

A Survey on Adversarial Attacks for Malware Analysis

IEEE Access
Machine learning-based malware analysis approaches are widely researched and deployed in critical infrastructures for detecting and classifying evasive and growing malware threats.
Kshitiz Aryal, Maanak Gupta, Mahmoud Abdelsalam, Pradip Kunwar, Bhavani Thuraisingham   +4 more
doaj   +1 more source

Adversarial Imitation Attack

, 2020
8 ...
Zhou, Mingyi, Wu, Jing, Liu, Yipeng, Huang, Xiaolin, Liu, Shuaicheng, Zhang, Xiang, Zhu, Ce   +6 more
openaire   +2 more sources

Adversarial Feature Selection Against Evasion Attacks [PDF]

IEEE Transactions on Cybernetics, 2016
Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed.
Zhang F, Chan PPK, BIGGIO, BATTISTA, Yeung DS, ROLI, FABIO   +4 more
openaire   +4 more sources

Materials and System Design for Self‐Decision Bioelectronic Systems

Advanced Materials, EarlyView.
This review highlights how self‐decision bioelectronic systems integrate sensing, computation, and therapy into autonomous, closed‐loop platforms that continuously monitor and treat diseases, marking a major step toward intelligent, self‐regulating healthcare technologies.
Qiankun Zeng, Hong Liu, Yongheng Zhang, Youbin Zheng, Xuyin Ding, Mengni Zhu, Guoyue Shi, Yan Wang, Hossam Haick, Min Zhang   +9 more
wiley   +1 more source