Results 51 to 60 of about 5,384 (200)
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 Accepted to WACV ...
Ke Xu +4 more
openaire +2 more sources
BMPCQA: Bioinspired Metaverse Point Cloud Quality Assessment Based on Large Multimodal Models
Advanced Intelligent Systems, EarlyView.This study presents a bioinspired metaverse point cloud quality assessment metric, which simulates the human visual evaluation process to perform the point cloud quality assessment task. It first extracts rendering projection video features, normal image features, and point cloud patch features, which are then fed into a large multimodal model to ...
Huiyu Duan +7 more
wiley +1 more source
Detecting Patch Adversarial Attacks with Image Residuals
CoRR, 2020 We introduce an adversarial sample detection algorithm based on image residuals, specifically designed to guard against patch-based attacks. The image residual is obtained as the difference between an input image and a denoised version of it, and a discriminator is trained to distinguish between clean and adversarial samples.
Marius Arvinte +2 more
openaire +2 more sources
Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining
Advanced Intelligent Systems, EarlyView.Calibration‐free electromyography motor intent decoding is enabled through large‐scale supervised pretraining across heterogeneous datasets. A Spatially Aware Feature‐learning Transformer processes variable channel counts and electrode geometries, allowing transfer across users and recording setups. On a held‐out benchmark, fine‐tuned cross‐user models
Alexander E. Olsson +3 more
wiley +1 more source
Perceptual-Sensitive GAN for Generating Adversarial Patches
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Recently, adversarial patch, with noise confined to a small and localized patch, emerged for its easy accessibility in real-world.
Aishan Liu +6 more
openaire +2 more sources
Patch-wise++ Perturbation for Adversarial Targeted Attacks
CoRR, 2020 Although great progress has been made on adversarial attacks for deep neural networks (DNNs), their transferability is still unsatisfactory, especially for targeted attacks. There are two problems behind that have been long overlooked: 1) the conventional setting of $T$ iterations with the step size of $ε/T$ to comply with the $ε$-constraint.
Lianli Gao +3 more
openaire +2 more sources
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 +6 more
wiley +1 more source
Adversarial Patch Attacks on Monocular Depth Estimation Networks
IEEE Access, 2020 Thanks to the excellent learning capability of deep convolutional neural networks (CNN), monocular depth estimation using CNNs has achieved great success in recent years.
Koichiro Yamanaka +3 more
doaj +1 more source
PAD: Patch-Agnostic Defense against Adversarial Patch Attacks
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Accepted by CVPR ...
Lihua Jing +4 more
openaire +2 more sources
Advanced Intelligent Systems, EarlyView.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source