计算机科学 ›› 2022, Vol. 49 ›› Issue (12): 340-345.doi: 10.11896/jsjkx.220500185
王晓明, 温旭云, 徐梦婷, 张道强
WANG Xiao-ming, WEN Xu-yun, XU Meng-ting, ZHANG Dao-qiang
摘要: 近年来,利用静息态功能磁共振成像的脑功能网络分析已被广泛应用于各类脑疾病的计算机辅助诊断任务中。结合临床表型测量与脑功能网络构建的图卷积神经网络框架,提高了智能医学疾病诊断模型对现实世界的适用性。但是,基于脑功能网络的疾病诊断模型的可信度研究是一个重要但仍被广泛忽视的部分。对抗攻击技术在医疗机器学习中对模型的“欺骗”进一步引发了模型应用于临床实际中的安全与信任问题。基于此,在这项工作中,首次提出了一种面向脑疾病诊断的图卷积网络对抗攻击方法BFGCNattack,结合临床表型测量构建了疾病诊断模型,探索评估了智能诊断模型在面临对抗攻击时的鲁棒性。在自闭症脑成像数据集上的实验结果表明,使用图卷积网络构建的诊断模型在面临提出的对抗攻击时是脆弱的,即使只执行少量(10%)的扰动,模型的准确率和分类裕度均显著下降,同时愚弄率也显著提高。
中图分类号:
[1]FINN E S,ROSENBERG M D.Beyond fingerprinting:Choosing predictive connectomes over reliable connectomes[J].Neuro-Image,2021,239:118254. [2]SHEN X L,FINN E S,SCHEINOST D,et al.Using connectome-based predictive modeling to predict individual behavior from brain connectivity[J].Nature Protocols,2017,12(3):506-518. [3]SONG H,FINN E S,ROSENBERG M D.Neural signatures of attentional engagement during narratives and its consequences for event memory[C]//Proceedings of the National Academy of Sciences.2021. [4]DU Y H,FU Z,CALHOUN V D.Classification and prediction of brain disorders using functional connectivity:promising but challenging[J/OL].https://www.frontiersin.org/articles/10.3389/fnins.2018.00525/full. [5]ZHANG D Q,HUANG J S,JIE B,et al.Ordinal pattern:A new descriptor for brain connectivity networks[J].IEEE Transactions on Medical Imaging,2018,37(7):1711-1722. [6]GAN J Z,PENG Z W,ZHU X F,et al.Brain functional connectivity analysis based on multi-graph fusion[J/OL].https://www.sciencedirect.com/science/article/abs/pii/S1361841521001031. [7]BENKARIM O,PAQUOLA C,PARK B,et al.The cost of untracked diversity in brain-imaging prediction[J/OL]. https://www.biorxiv.org/content/10.1101/2021.06.16.448764v1. [8]SZEGEDY C,ZAREMBA W,SUTSKEVER I,et al.Intriguing properties of neural networks[J].arXiv:1312.6199,2013. [9]DEMONTIS A,MELIS M,BIGGIO B,et al.Yes,machine lear-ning can be more secure! a case study on android malware detection[J].IEEE Transactions on Dependable and Secure Computing,2017,16(4):711-724. [10]GOODFELLOW I J,SHLENS J,SZEGEDY C.Explaining and harnessing adversarial examples[J].arXiv:1412.6572,2014. [11]FINLAYSON S G,BOWERS J D,ITO J,et al.Adversarial attacks on medical machine learning[J].Science,2019,363(6433):1287-1289. [12]DI MARTINO A,YAN C G,LI Q,et al.The autism brain imaging data exchange:towards a large-scale evaluation of the intrinsic brain architecture in autism[J].Molecular psychiatry,2014,19(6):659-667. [13]SUN L C,DOU Y T,YANG C,et al.Adversarial attack and defense on graph data:A survey[J].arXiv:1812.10528,2018. [14]XU K D,CHEN H G,LIU S J,et al.Topology attack and defense for graph neural networks:An optimization perspective[J].arXiv:1906.04214,2019. [15]DOU Y T,MA G X,YU P S,et al.Robust spammer detection by nash reinforcement learning[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Disco-very & Data Mining.2020:924-933. [16]WANG X Y,CHENG M H,EATON J,et al.Attack graph convolutional networks by adding fake nodes[J].arXiv:1810.10751,2018. [17]ZÜGNER D,AKBARNEJAD A,GÜNNEMANN S.Adversarial attacks on neural networks for graph data[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2018:2847-2856. [18]CHEN J Y,CHEN L H,CHEN Y X,et al.GA-based Q-attack on community detection[J].IEEE Transactions on Computational Social Systems,2019,6(3):491-503. [19]BOJCHEVSKI A,GÜNNEMANN S.Adversarial attacks onnode embeddings via graph poisoning[C]//InternationalConfe-rence on Machine Learning.PMLR,2019:695-704. [20]CHEN Y Z,NADJI Y,KOUNTOURAS A,et al.Practical attacks against graph-based clustering[C]//Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security.2017:1125-1142. [21]FENG F L,HE X N,TANG J,et al.Graph adversarial training:Dynamically regularizing based on graph structure[J].IEEE Transactions on Knowledge and Data Engineering,2019,33(6):2493-2504. [22]BETZEL R F,BASSETT D S.Multi-scale brain networks[J].Neuroimage,2017,160:73-83. [23]GREICIUS M D,KRASNOW B,REISS A L,et al.Functional connectivity in the resting brain:a network analysis of the default mode hypothesis[C]//Proceedings of the National Academy of Sciences.2003:253-258. [24]KTENA S I,PARISOT S,FERRANTE E,et al.Metric learning with spectral graph convolutions on brain connectivity networks[J].NeuroImage,2018,169:431-442. [25]PARISOT S,KTENA S I,FERRANTE E,et al.Disease prediction using graph convolutional networks:application to autism spectrum disorder and Alzheimer’s disease[J].Medical Image Analysis,2018,48:117-130. [26]DEFFERRARD M,BRESSON X,VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering[J].Advances in Neural Information Processing Systems,2016,29:3844-3852. [27]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016. |
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