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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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An enhanced ensemble defense framework for boosting adversarial robustness of intrusion detection systems [PDF]
Scientific ReportsMachine learning (ML) and deep neural networks (DNN) have emerged as powerful tools for enhancing intrusion detection systems (IDS) in cybersecurity.
Zeinab Awad, Magdy Zakaria, Rasha Hassan
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Diversity-enhanced reconstruction as plug-in defenders against adversarial perturbations [PDF]
Frontiers in Artificial IntelligenceDeep learning models are susceptible to adversarial examples. In large-scale deployed services, plug-in defenders efficiently defend against such attacks.
Zeshan Pang +7 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
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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
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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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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
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Adversarial example defense algorithm for MNIST based on image reconstruction
网络与信息安全学报, 2022 With the popularization of deep learning, more and more attention has been paid to its security issues.The adversarial sample is to add a small disturbance to the original image, which can cause the deep learning model to misclassify the image, which ...
Zhongyuan QIN +3 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
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IEEE Access, 2022 An adversarial example, which is an input instance with small, intentional feature perturbations to machine learning models, represents a concrete problem in Artificial intelligence safety.
Seok-Hwan Choi +3 more
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