Results 91 to 100 of about 1,236,597 (154)

Benign-salient Region Based End-to-End Adversarial Malware Generation Method [PDF]

Jisuanji kexue
Malware detection methods combining visualization techniques and deep learning have gained widespread attention due to their high accuracy and low cost.However,deep learning models are vulnerable to adversarial attacks,where intentional small-scale ...
YUAN Mengjiao, LU Tianliang, HUANG Wanxin, HE Houhan
doaj   +1 more source

SURVEY OF ADVERSARIAL ATTACKS AND DEFENSE AGAINST ADVERSARIAL ATTACKS

Darpan International Research Analysis
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, Sanskar Agarwal, Armaan Pareek, Vanshika Singh   +3 more
openaire   +1 more source

Causality adversarial attack generation algorithm for intelligent unmanned communication system

Tongxin xuebao
A causality adversarial attack generation algorithm was proposed in response to the causality issue of gradient-based adversarial attack generation algorithms in practical communication system.The sequential input-output features and temporal memory ...
Shuwen YU, Wei XU, Jiacheng YAO
doaj   +2 more sources

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

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

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

Mathematical Analysis of Adversarial Attacks

CoRR, 2018
In this paper, we analyze efficacy of the fast gradient sign method (FGSM) and the Carlini-Wagner's L2 (CW-L2) attack. We prove that, within a certain regime, the untargeted FGSM can fool any convolutional neural nets (CNNs) with ReLU activation; the targeted FGSM can mislead any CNNs with ReLU activation to classify any given image into any prescribed
Zehao Dou, Stanley J. Osher, Bao Wang 0001   +2 more
openaire   +2 more sources

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

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

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