Results 61 to 70 of about 85,609 (269)

Multi-Task Adversarial Attack

CoRR, 2020
Deep neural networks have achieved impressive performance in various areas, but they are shown to be vulnerable to adversarial attacks. Previous works on adversarial attacks mainly focused on the single-task setting. However, in real applications, it is often desirable to attack several models for different tasks simultaneously. To this end, we propose
Pengxin Guo, Yuancheng Xu, Baijiong Lin, Yu Zhang 0006   +3 more
openaire   +2 more sources

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Evaluating Pretrained Deep Learning Models for Image Classification Against Individual and Ensemble Adversarial Attacks

IEEE Access
The robustness of Deep Neural Networks (DNNs) against adversarial attacks is an important topic in the area of deep learning. To fully investigate the robustness of DNNs, this study examines four frequently used white box adversarial attack techniques ...
Mafizur Rahman, Prosenjit Roy, Sherri S. Frizell, Lijun Qian   +3 more
doaj   +1 more source

A Robust Adversarial Example Attack Based on Video Augmentation

Applied Sciences, 2023
Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems.
Mingyong Yin, Yixiao Xu, Teng Hu, Xiaolei Liu   +3 more
doaj   +1 more source

Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source

A new method for countering evasion adversarial attacks on information systems based on artificial intelligence

Научно-технический вестник информационных технологий, механики и оптики
Modern artificial intelligence (AI) technologies are being used in a variety of fields, from science to everyday life. However, the widespread use of AI-based systems has highlighted a problem with their vulnerability to adversarial attacks.
A. A. Vorobeva, M. A. Matuzko, D. I. Sivkov, R. I. Safiullin, A. A. Menshchikov   +4 more
doaj   +1 more source

Zero Watermarking Using Convolutional Additive Self‐Attention Vision Transformer and Discrete Wavelet Transform‐Variance‐Based Feature Descriptor for Medical Image Security in Mobile Healthcare Services

Advanced Intelligent Systems, EarlyView.
A zero‐watermarking algorithm that combines a refined convolutional additive self‐attention vision transformer (CAS‐ViT) with a discrete wavelet transform variance‐based feature descriptor (DVFD) is proposed for protecting the privacy of medical images in mobile healthcare services.
Pei Liu, Yan Yan, Hao Huang, Ping Wang, Weirong Qin, Xiangjian Zuo, Yongqin Li   +6 more
wiley   +1 more source

A Survey on Efficient Methods for Adversarial Robustness

IEEE Access, 2022
Deep learning has revolutionized computer vision with phenomenal success and widespread applications. Despite impressive results in complex problems, neural networks are susceptible to adversarial attacks: small and imperceptible changes in input space ...
Awais Muhammad, Sung-Ho Bae
doaj   +1 more source

An Intelligent Feature Engineering‐Driven Hybrid Framework for Adversarial Domain Name System Tunneling Detection

Advanced Intelligent Systems, EarlyView.
This study presents a novel framework that enhances the reliability of DNS traffic monitoring using a hybrid long short‐term memory‐deep neural network (LSMT‐DNN) architecture, enabling robust detection of adversarial DNS tunneling. The proposed framework leverages feature extraction from DNS traffic patterns, including domain request sequences, query ...
Ahmad Almadhor, Shtwai Alsubai, Monji Mohamed Zaidi, Natalia Kryvinska, Abdullah Al Hejaili, Sidra Abbas   +5 more
wiley   +1 more source

Multiple Adversarial Domains Adaptation Approach for Mitigating Adversarial Attacks Effects

International Transactions on Electrical Energy Systems, 2022
Although neural networks are near achieving performance similar to humans in many tasks, they are susceptible to adversarial attacks in the form of a small, intentionally designed perturbation, which could lead to misclassifications.
Bader Rasheed, Adil Khan, Muhammad Ahmad, Manuel Mazzara, S. M. Ahsan Kazmi   +4 more
doaj   +1 more source