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Adversarial patch defense algorithm based on PatchTracker

网络与信息安全学报
The application of deep neural networks in target detection has been widely adopted in various fields.However, the introduction of adversarial patch attacks, which add local perturbations to images to mislead deep neural networks, poses a significant ...
Zhenjie XIAO, Shiyu HUANG, Feng YE, Liqing HUANG, Tianqiang HUANG
doaj   +3 more sources

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

Dual-Branch Spectral–Spatial Adversarial Representation Learning for Hyperspectral Image Classification With Few Labeled Samples

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Recently, deep learning methods, particularly the convolutional neural networks, have been extensively employed for extracting spectral–spatial features in hyperspectral image (HSI) classification tasks, yielding promising results.
Caihao Sun, Xiaohua Zhang, Hongyun Meng, Xianghai Cao, Jinhua Zhang, Licheng Jiao   +5 more
doaj   +1 more source

Benchmarking Adversarial Patch Against Aerial Detection

IEEE Transactions on Geoscience and Remote Sensing, 2022
DNNs are vulnerable to adversarial examples, which poses great security concerns for security-critical systems. In this paper, a novel adaptive-patch-based physical attack (AP-PA) framework is proposed, which aims to generate adversarial patches that are adaptive in both physical dynamics and varying scales, and by which the particular targets can be ...
Jiawei Lian, Shaohui Mei, Shun Zhang, Mingyang Ma 0004   +3 more
openaire   +2 more sources

Distributional Modeling for Location-Aware Adversarial Patches

CoRR, 2023
Adversarial patch is one of the important forms of performing adversarial attacks in the physical world. To improve the naturalness and aggressiveness of existing adversarial patches, location-aware patches are proposed, where the patch's location on the target object is integrated into the optimization process to perform attacks.
Xingxing Wei 0001, Shouwei Ruan, Yinpeng Dong, Hang Su 0006   +3 more
openaire   +2 more sources

URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection

Mathematics
In recent years, deep learning has been extensively deployed on unmanned aerial vehicles (UAVs), particularly for object detection. As the cornerstone of UAV-based object detection, deep neural networks are susceptible to adversarial attacks, with ...
Hailong Xi, Le Ru, Jiwei Tian, Bo Lu, Shiguang Hu, Wenfei Wang, Xiaohui Luan   +6 more
doaj   +1 more source

Saliency guided data augmentation strategy for maximally utilizing an object’s visual information

PLoS ONE, 2022
Among the various types of data augmentation strategies, the mixup-based approach has been particularly studied. However, in existing mixup-based approaches, object loss and label mismatching can occur if random patches are utilized when constructing ...
Junhyeok An, Soojin Jang, Junehyoung Kwon, Kyohoon Jin, YoungBin Kim   +4 more
doaj   +2 more sources

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

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

Infrared Adversarial Patch Generation Based on Reinforcement Learning

Mathematics
Recently, there has been an increasing concern about the vulnerability of infrared object detectors to adversarial attacks, where the object detector can be easily spoofed by adversarial samples with aggressive patches.
Shuangju Zhou, Yang Li, Wenyi Tan, Chenxing Zhao, Xin Zhou, Quan Pan   +5 more
doaj   +1 more source