Results 51 to 60 of about 527,315 (106)

An adversarial attack method based on pixel location characteristics

Journal of Intelligent Systems
Deep learning techniques have been widely used in various fields. However, they face significant security challenges due to the existence of adversarial examples.
Zhao Qin, Liu Jiuhong, Ma Xian, Liu Tongbo, Du Yingzhen   +4 more
doaj   +1 more source

Review of Research on Adversarial Attack in Three Kinds of Images [PDF]

Jisuanji kexue yu tansuo
In recent years, there have been numerous breakthroughs in deep learning, leading to the expansion of applications based on deep learning into a wide range of fields.
XU Yuhui, PAN Zhisong, XU Kun
doaj   +1 more source

Ctta: a novel chain-of-thought transfer adversarial attacks framework for large language models

Cybersecurity
Recent studies have indicated that large language models (LLMs) remain susceptible to adversarial attacks, despite enhanced robustness through the chain-of-thought (CoT) capability.
Xinxin Yue, Zhiyong Zhang, Junchang Jing, Weiguo Wang   +3 more
doaj   +1 more source

SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images [PDF]

Adversarial attack has garnered considerable attention due to its profound implications for the secure deployment of robots in sensitive security scenarios.
Fan, Mingyuan, Chen, Cen, Liu, Ximeng, Liu, Yang   +3 more
core   +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

tsubasa512/ADM_Adversarial_attack: ADM adversarial attack

<p>Adaptive Deformation Method (ADM) is an adversarial attack against the one stage object detection in ORSIs</p ...
tsubasa512
core   +1 more source

Llm-ga: A gradient-based multi-label adversarial attack by large language models

Complex & Intelligent Systems
Deep neural networks (DNNs) are highly sensitive to small, meticulously crafted perturbations, which have been utilized in adversarial attacks, threatening the reliability of DNNs in practical applications. Current adversarial attack methods rely heavily
Yujiang Liu, Yamin Hu, Zhijian Chen, Shiyin Wang, Wenjian Luo   +4 more
doaj   +1 more source

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

DeeBBAA: A Benchmark Deep Black Box Adversarial Attack Against Cyber-Physical Power Systems

Cyber-physical infrastructure faces threats from evasive false data injection attacks that can significantly impact their security and performance. Adversarial attacks are a popular evasive false data injection threat model that generally targets AI/ML ...
Tushar, W, Saha, TK, Bhattacharjee, A, Mishra, S, Verma, A, Bai, G   +5 more
core   +1 more source