Results 51 to 60 of about 447,373 (304)

Multiple Classifier Systems in Adversarial Environments: "Challenges and Solutions" [PDF]

, 2009
Pattern recognition methods offer technological background for a variety of applications in a modern information society. They are however undermined by several kinds of "adversarial" misuses like email and web spam, attacks to computer networks, etc.
Gargiulo, Francesco
core   +1 more source

Algebraic adversarial attacks on explainability models

Machine Learning. Engineering
Classical adversarial attacks are phrased as a constrained optimisation problem. Despite the efficacy of a constrained optimisation approach to adversarial attacks, one cannot trace how an adversarial point was generated.
Lachlan Simpson, Federico Costanza, Kyle Millar, Adriel Cheng, Cheng-Chew Lim, Hong Gunn Chew   +5 more
doaj   +1 more source

StackingNet: Collective Inference Across Independent AI Foundation Models

Advanced Science, EarlyView.
ABSTRACT Artificial intelligence (AI) built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readily share their capabilities. Coordinating the complementary strengths of independently developed, black‐box foundation models is essential for trustworthy ...
Siyang Li, Chenhao Liu, Dongrui Wu, Zhigang Zeng, Lieyun Ding   +4 more
wiley   +1 more source

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 Attacks on Human Vision

CoRR, 2022
This article presents an introduction to visual attention retargeting, its connection to visual saliency, the challenges associated with it, and ideas for how it can be approached. The difficulty of attention retargeting as a saliency inversion problem lies in the lack of one-to-one mapping between saliency and the image domain, in addition to the ...
Victor A. Mateescu, Ivan V. Bajic
openaire   +2 more sources

Time Resolved DNA Barcodes for Information Encoding and Dynamic Encryption

Advanced Science, EarlyView.
This study establishes a molecular information platform based on DNA Temporal Barcodes. Information is encoded through combinations of DNA tags with distinct retention times, while dynamic encryption is achieved through a key‐triggered DNA ligation.
Likang Chu, Haixia Wang, Haiyan Gao, Sixian Chen, Zilong Li, Zhanyihao Hao, Lei He, Da Han   +7 more
wiley   +1 more source

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

Robust ConvLSTM Model With Deep Reinforcement Learning for Stealth Attack Detection in Smart Grids

IEEE Open Journal of the Industrial Electronics Society
The advent of modern electricity distribution systems, comprising digital communication technologies and principles, has triggered a new era of smart grids, in which advanced metering infrastructure plays a crucial role in functions, such as digital ...
Ahmad N. Alkuwari, Abdullatif Albaseer, Saif Al-Kuwari, Marwa Qaraqe   +3 more
doaj   +1 more source

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

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source