GGT: Graph-guided testing for adversarial sample detection of deep neural network-Reference-Cited by-同舟云学术

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GGT: Graph-guided testing for adversarial sample detection of deep neural network

Published:2024-05 Issue: Volume:140 Page:103710
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Chen Zuohui^ORCID,Wang Renxuan,Xiang Jingyang^ORCID,Yu Yue^ORCID,Xia Xin,Ji Shouling,Xuan Qi,Yang Xiaoniu

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Publisher

Elsevier BV

Reference69 articles.

1. Adversarial example detection using latent neighborhood graph;Abusnaina,2021

2. Damad: database, attack, and model agnostic adversarial perturbation detector;Agarwal;IEEE Trans. Neural Netw. Learn. Syst.,2021

3. Graph tools for social network analysis;Akhtar,2021

4. Adversarial example detection for dnn models: a review and experimental comparison;Aldahdooh;Artif. Intell. Rev.,2022

5. Iot device fingerprint using deep learning;Aneja,2018

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advanced progress of network resilience;Europhysics Letters;2024-04-01

2. Single-Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning;IEEE Transactions on Computational Social Systems;2024

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