Results 91 to 100 of about 8,712 (199)

Enhancing the Transferability of Adversarial Patch via Alternating Minimization

International Journal of Computational Intelligence Systems
Adversarial patches, a type of adversarial example, pose serious security threats to deep neural networks (DNNs) by inducing erroneous outputs. Existing gradient stabilization methods aim to stabilize the optimization direction of adversarial examples ...
Yang Wang, Lei Chen, Zhen Yang, Tieyong Cao   +3 more
doaj   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source

User‐Based Evaluation of Explainability Techniques for Misogyny Detection in Code‐Mixed Hindi–English

Applied AI Letters, Volume 7, Issue 2, June 2026.
In this work, we have performed human‐based evaluation of three post hoc explainability techniques, Local Interpretable Model Agnostic Explanations (LIME), Shapely Additive Explanations (SHAP), and integrated Gradients (IG) for a multilingual Bidirectional Encoder Representations from Transformers (mBERT) based binary and multi‐label misogyny ...
Sargam Yadav, Abhishek Kaushik, Kevin McDaid   +2 more
wiley   +1 more source

Deep Learning for Satellite‐Based Forest Disturbance Monitoring: Recent Advances and Challenges

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.
Overview of key research challenges in forest disturbance monitoring, including the detection of disturbances of varying severity, the attribution of disturbance agents, and the development of models capable of generalizing across regions. ABSTRACT Climate change and land use pressures are intensifying forest disturbances in many world regions, as ...
Carolina Natel, Christoph Molnar, Ricardo Dalagnol, Peer Nowack   +3 more
wiley   +1 more source

Optimal Adversarial Sample Generation Method in SAR ATR Based on Joint Misleading and Fidelity Optimization

Leida xuebao
Adversarial sample generation is a key research direction for uncovering the vulnerabilities of deep neural networks and improving the robustness of Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) systems.
Xinyuan SU, Sinong QUAN, Zhihao CAI, Shiqi XING, Junpeng WANG   +4 more
doaj   +1 more source

Adversarial Patch Attack for Ship Detection via Localized Augmentation

CoRR
Current ship detection techniques based on remote sensing imagery primarily rely on the object detection capabilities of deep neural networks (DNNs). However, DNNs are vulnerable to adversarial patch attacks, which can lead to misclassification by the detection model or complete evasion of the targets.
Chun Liu 0008, Panpan Ding, Zheng Zheng, Hailong Wang, Bingqian Zhu, Tao Xu, Zhigang Han, Jiayao Wang   +7 more
openaire   +2 more sources

Relational legal consciousness and the mobilization of the law of the inquest in England and Wales

Journal of Law and Society, Volume 53, Issue 2, Page 291-314, June 2026.
Abstract This article explores the legal consciousness of bereaved people in contact with the coronial system in England and Wales, drawing on an interview‐based empirical study. Informed by socio‐legal scholarship on relational dimensions of legal consciousness and citizens’ mobilization of the law, the article analyses the relationships within and ...
JESSICA JACOBSON, LORNA TEMPLETON, ALEXANDRA MURRAY   +2 more
wiley   +1 more source

Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling

Advanced Healthcare Materials, Volume 15, Issue 17, 8 May 2026.
Emerging advances in three‐dimensional bioprinting and computational modeling are reshaping cardiovascular (CV) research by enabling more realistic, patient‐specific tissue platforms. This review surveys cutting‐edge approaches that merge biomimetic CV constructs with computational simulations to overcome the limitations of traditional models, improve ...
Tanmay Mukherjee, Mehdi Salar Amoli, Sarah Rezapourdamanab, Lama Rita El Shammas, Martin L. Tomov, Emilio A. Mendiola, Vahid Serpooshan, Reza Avazmohammadi   +7 more
wiley   +1 more source

Evaluating the Utilities of Foundation Models in Single‐Cell Data Analysis

Advanced Science, Volume 13, Issue 27, 13 May 2026.
This study delivers the first systematic, task‐level evaluation of single‐cell foundation models across eight core analytical tasks. By benchmarking 10 leading models with the scEval framework, it reveals where foundation models truly add value, where task‐specific methods still dominate, and provides concrete, reproducible guidelines to steer the next
Tianyu Liu, Kexing Li, Yuge Wang, Hongyu Li, Hongyu Zhao   +4 more
wiley   +1 more source

Block-level masking and feature importance-based adversarial example generation

Journal of Big Data
This paper proposes a method to enhance the transferability of adversarial examples by combining a Learnable Patch-Wise Mask (LPM) generated through differential evolution algorithm with a Feature Importance Aware (FIA) attack.
Wenbo Qiu, Yafei Song
doaj   +1 more source