Results 71 to 80 of about 5,389,393 (319)

2021 17th International Conference on Mobility, Sensing and Networking (MSN)

, 2021
Federated learning has attracted attention in recent years due to its native privacy-preserving features. However, it is still vulnerable to various membership inference attacks, such as backdoor, poisoning, and adversarial attacks.
Chen, Bing, Xie, Yuanyuan, Zhang, Jiale, Wu, Di   +3 more
core   +1 more source

Human-Producible Adversarial Examples

CoRR, 2023
Submitted to ICLR ...
David Khachaturov, Yue Gao 0011, Ilia Shumailov, Robert Mullins 0001, Ross J. Anderson, Kassem Fawaz   +5 more
openaire   +2 more sources

Adversarial Example Detection by Classification for Deep Speech Recognition [PDF]

IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019
Machine Learning systems are vulnerable to adversarial attacks and will highly likely produce incorrect outputs under these attacks. There are white-box and black-box attacks regarding to adversary’s access level to the victim learning algorithm.
Saeid Samizade, Zheng-Hua Tan, Chao Shen, X. Guan   +3 more
semanticscholar   +1 more source

POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI

IEEE Access
Adversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence.
Han-Ju Lee, Jin-Seoung Kim, Han-Jin Lee, Seok-Hwan Choi   +3 more
doaj   +1 more source

Image Classification Adversarial Example Defense Method Based on Conditional Diffusion Model [PDF]

Jisuanji gongcheng
Deep-learning models have achieved impressive results in fields such as image classification; however, they remain vulnerable to interference and threats from adversarial examples.
CHEN Zimin, GUAN Zhitao
doaj   +1 more source

Simple Transparent Adversarial Examples

CoRR, 2021
There has been a rise in the use of Machine Learning as a Service (MLaaS) Vision APIs as they offer multiple services including pre-built models and algorithms, which otherwise take a huge amount of resources if built from scratch. As these APIs get deployed for high-stakes applications, it's very important that they are robust to different ...
Jaydeep Borkar, Pin-Yu Chen
openaire   +2 more sources

DTFA: Adversarial attack with discrete cosine transform noise and target features on deep neural networks

IET Image Processing, 2023
Image recognition on deep neural network is vulnerable to adversarial sample attacks. The adversarial attack accuracy is low when only limited queries on the target are allowed with the current black box environment.
Dong Yang, Wei Chen, Songjie Wei
doaj   +1 more source

Detecting Adversarial Examples

CoRR
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples. While numerous successful adversarial attacks have been proposed, defenses against these attacks remain relatively understudied. Existing defense approaches either focus on negating the effects of perturbations caused by the attacks to restore the DNNs' original ...
Furkan Mumcu, Yasin Yilmaz 0001
openaire   +2 more sources

Adversarial Attacks to Manipulate Target Localization of Object Detector

IEEE Access
Adversarial attack has gradually become an important branch in the field of artificial intelligence security, where the potential threat brought by adversarial example attack is more not to be ignored.
Kai Xu, Xiao Cheng, Ji Qiao, Jia-Teng Li, Kai-Xuan Ji, Jia-Yong Zhong, Peng Tian, Jian-Xun Mi   +7 more
doaj   +1 more source

Detecting Audio Adversarial Examples in Automatic Speech Recognition Systems Using Decision Boundary Patterns

Journal of Imaging, 2022
Automatic Speech Recognition (ASR) systems are ubiquitous in various commercial applications. These systems typically rely on machine learning techniques for transcribing voice commands into text for further processing.
Wei Zong, Yang-Wai Chow, Willy Susilo, Jongkil Kim, Ngoc Thuy Le   +4 more
doaj   +1 more source