Results 11 to 20 of about 4,324 (256)
LPF-Defense: 3D adversarial defense based on frequency analysis [PDF]
PLOS ONE, 2023 The 3D point clouds are increasingly being used in various application including safety-critical fields. It has recently been demonstrated that deep neural networks can successfully process 3D point clouds. However, these deep networks can be misclassified via 3D adversarial attacks intentionality designed to perturb some point cloud’s features.
Hanieh Naderi +3 more
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Open-Set Adversarial Defense with Clean-Adversarial Mutual Learning [PDF]
International Journal of Computer Vision, 2022 Accepted by International Journal of Computer Vision (IJCV) 2022. Code will be available at https://github.com/rshaojimmy/ECCV2020-OSAD.
Vishal M Patel, Pong Chi Yuen, Rui Shao
exaly +3 more sources
Universal attention guided adversarial defense using feature pyramid and non-local mechanisms [PDF]
Scientific ReportsDeep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples, significantly hindering the development of deep learning technologies in high-security domains. A key challenge is that current defense methods often lack universality,
Jiawei Zhao +6 more
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Survey of Image Adversarial Example Defense Techniques [PDF]
Jisuanji kexue yu tansuo, 2023 The rapid and extensive growth of artificial intelligence introduces new security challenges. The generation and defense of adversarial examples for deep neural networks is one of the hot spots.
LIU Ruiqi, LI Hu, WANG Dongxia, ZHAO Chongyang, LI Boyu
doaj +1 more source
Adversarial Sample Defense Method Based on Noise Dissolution [PDF]
Jisuanji gongcheng, 2022 The security problems exposed in the rapid development of the Deep Neural Network(DNN) have gradually attracted our attention.However, since adversarial examples were first defined, many adversarial attacks on DNNs have been proposed, and the complexity ...
YANG Wenxue, WU Fei, GUO Tong, XIAO Limin
doaj +1 more source
Research Progress of Adversarial Defenses on Graphs
Jisuanji kexue yu tansuo, 2021 Graph neural networks (GNN) have been successfully applied in complex tasks in many fields, but recent studies show that GNN is vulnerable to graph adversarial attacks, leading to severe performance degradation.
LI Penghui, ZHAI Zhengli, FENG Shu
doaj +1 more source
Survey of Adversarial Attacks and Defense Methods for Deep Learning Model [PDF]
Jisuanji gongcheng, 2021 As an important part of artificial intelligence technology,deep learning is widely used in computer vision,natural language processing and other fields.Although deep learning performs well in tasks such as image classification and target detection,its ...
JIANG Yan, ZHANG Liguo
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Muzammal Naseer +4 more
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Clustering Approach for Detecting Multiple Types of Adversarial Examples
Sensors, 2022 With intentional feature perturbations to a deep learning model, the adversary generates an adversarial example to deceive the deep learning model.
Seok-Hwan Choi +3 more
doaj +1 more source
CoRR, 2023 In response to the rapidly evolving nature of adversarial attacks against visual classifiers, numerous defenses have been proposed to generalize against as many known attacks as possible. However, designing a defense method that generalizes to all types of attacks is unrealistic, as the environment in which the defense system operates is dynamic.
Qian Wang 0001 +8 more
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