Results 91 to 100 of about 6,497 (247)

Distillation-Based Cross-Model Transferable Adversarial Attack for Remote Sensing Image Classification

Remote Sensing
Deep neural networks have achieved remarkable performance in remote sensing image (RSI) classification tasks. However, they remain vulnerable to adversarial attack.
Xiyu Peng, Jingyi Zhou, Xiaofeng Wu
doaj   +1 more source

RLXSS: Optimizing XSS Detection Model to Defend Against Adversarial Attacks Based on Reinforcement Learning

Future Internet, 2019
With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. Adversarial attacks against artificial intelligence models become inevitable problems when there is a
Yong Fang, Cheng Huang, Yijia Xu, Yang Li   +3 more
doaj   +1 more source

Multimodal artificial intelligence in medical biotechnology: Integrating genomics, imaging, and clinical data for precision therapeutics

Precision Medical Sciences, EarlyView.
Overview of multimodal artificial intelligence (AI) for precision therapeutics. Diverse biomedical data modalities, including multi‐omics, medical imaging, digital pathology, electronic health records, wearable‐device data, and molecular information, are integrated through multimodal AI frameworks incorporating fusion strategies, foundation models ...
Gedion Mengistu Dejen
wiley   +1 more source

Physically structured adversarial patch inspired by natural leaves multiply angles deceives infrared detectors

Journal of King Saud University: Computer and Information Sciences
Researching infrared adversarial attacks is crucial for ensuring the safe deployment of security-sensitive systems reliant on infrared object detectors.
Zhiyang Hu, Xing Yang, Jiwen Zhao, Haoqi Gao, Haoli Xu, Hua Mu, Yangyang Wang   +6 more
doaj   +1 more source

Integrating multimodal data and machine learning for entrepreneurship research

Strategic Entrepreneurship Journal, EarlyView.
Abstract Research Summary Extant research in neuroscience suggests that human perception is multimodal in nature—we model the world integrating diverse data sources such as sound, images, taste, and smell. Working in a dynamic environment, entrepreneurs are expected to draw on multimodal inputs in their decision making.
Yash Raj Shrestha, Vivianna Fang He
wiley   +1 more source

DIPA: Adversarial Attack on DNNs by Dropping Information and Pixel-Level Attack on Attention

Information
Deep neural networks (DNNs) have shown remarkable performance across a wide range of fields, including image recognition, natural language processing, and speech processing. However, recent studies indicate that DNNs are highly vulnerable to well-crafted
Jing Liu, Huailin Liu, Pengju Wang, Yang Wu, Keqin Li   +4 more
doaj   +1 more source

Adversarial Attacks and Defences: A Survey

CoRR, 2018
Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using the traditional machine learning techniques in the past. In the last few years, deep learning has advanced radically in such a way that it can surpass human-level performance on a number ...
Anirban Chakraborty 0003, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, Debdeep Mukhopadhyay   +4 more
openaire   +2 more sources

Geometry‐Encoded Programmable Mechanics in Single‐Material Dual‐Phase Metamaterials

Small, EarlyView.
Inspired by the unique soft‐hard heterogeneous architecture of nacre, this study proposes a novel class of dual‐phase (DP) metamaterials. The DP metamaterials exhibit programmable nonlinear mechanical responses, tailored failure processes, and enhanced energy absorption by systematically varying the spatial coding patterns of soft‐hard unit cells ...
Miao Zhao, Na Qiu, Xuhui Zhang, Bei Peng, Zhi Zeng, Xinwei Li, Qing Li   +6 more
wiley   +1 more source

Black-Box Universal Adversarial Attack for DNN-Based Models of SAR Automatic Target Recognition

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Synthetic aperture radar automatic target recognition (SAR-ATR) models based on deep neural networks (DNNs) are vulnerable to attacks of adversarial examples. Universal adversarial attack algorithms can help evaluate and improve the robustness of the SAR-
Xuanshen Wan, Wei Liu, Chaoyang Niu, Wanjie Lu, Meng Du, Yuanli Li   +5 more
doaj   +1 more source

Adversarial Attacks on Hyperbolic Networks

As hyperbolic deep learning grows in popularity, so does the need for adversarial robustness in the context of such a non-Euclidean geometry. To this end, this paper proposes hyperbolic alternatives to the commonly used FGM and PGD adversarial attacks.
Max van Spengler, Jan Zahálka, Pascal Mettes   +2 more
openaire   +4 more sources