Results 61 to 70 of about 85,688 (262)
Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models
AI‐empowered catalysis research via integrated database platform, universal machine learning interatomic potentials (MLIPs), and large language models (LLMs). ABSTRACT The integration of artificial intelligence (AI) into catalysis is fundamentally reshaping the research paradigm of catalyst discovery.
Di Zhang +7 more
wiley +2 more sources
Deep neural networks (DNNs) have achieved great success in various applications due to their strong expressive power. However, recent studies have shown that DNNs are vulnerable to adversarial examples, and these manipulated instances can mislead DNN ...
Jianyi Liu +4 more
doaj +1 more source
A Distributed Biased Boundary Attack Method in Black-Box Attack
The adversarial samples threaten the effectiveness of machine learning (ML) models and algorithms in many applications. In particular, black-box attack methods are quite close to actual scenarios.
Fengtao Xiang +3 more
doaj +1 more source
Functional Adversarial Attacks
Accepted to NeurIPS ...
Laidlaw, Cassidy, Feizi, Soheil
openaire +2 more sources
Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare
Multimodal biosensing meets multidomain AI. Wearable biosensors capture complementary biochemical and physiological signals, while cross‐device, population‐aware learning aligns noisy, heterogeneous streams. This Review distills key sensing modalities, fusion and calibration strategies, and privacy‐preserving deployment pathways that transform ...
Chenshu Liu +10 more
wiley +1 more source
As with classification models, object detection models are vulnerable to adversarial attacks. In particular, adversarial attacks on key components of object detection models such as Region Proposal Network (RPN) and Non-Maximum Suppression (NMS ...
Gwang-Nam Kim +4 more
doaj +1 more source
SURVEY OF ADVERSARIAL ATTACKS AND DEFENSE AGAINST ADVERSARIAL ATTACKS
In recent years, the fields of Artificial Intelligence (AI) and Deep learning (DL) techniques along with Neural Networks (NNs) have shown great progress and scope for future research. Along with all the developments comes the threats and security vulnerabilities to Neural Networks and AI models. A few fabricated inputs/samples can lead to deviations in
Akshat Jain +3 more
openaire +1 more source
ABSTRACT Conventional software‐based encryption faces mounting limitations in power efficiency and security, inspiring the development of emerging neuromorphic computing hardware encryption. This study presents a hardware‐level multi‐dimensional encryption paradigm utilizing optoelectronic neuromorphic devices with low energy consumption of 3.3 fJ ...
Bo Sun +3 more
wiley +1 more source
DE-JSMA: a sparse adversarial attack algorithm for SAR-ATR models
The vulnerability of DNN makes the SAR-ATR system that uses an intelligent algorithm for recognition also somewhat vulnerable. In order to verify the vulnerability, this paper proposes DE-JSMA, a novel sparse adversarial attack algorithm based on a ...
JIN Xiaying, LI Yang, PAN Quan
doaj +1 more source
A concealable physical unclonable function (PUF) based on an array of 384 nanoscale voltage‐controlled magnetic tunnel junctions is demonstrated. The PUF operates without any external magnetic field. It uses a combination of deterministic and stochastic switching mechanisms, based on the spin transfer torque and voltage‐controlled magnetic anisotropy ...
Thomas Neuner +6 more
wiley +1 more source

