计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 1-8.doi: 10.11896/jsjkx.210300306
所属专题： 人工智能安全
景慧昀1, 魏薇1, 周川2,3, 贺欣4
JING Hui-yun1, WEI Wei1, ZHOU Chuan2,3, HE Xin4
摘要： 随着人工智能时代的到来,各行各业均开始结合自身业务需要部署人工智能系统,这全面加速了全球范围内人工智能规模化部署和应用进程。然而,人工智能基础设施、设计研发以及融合应用过程中面临的安全风险也随之而来。为了充分规避风险,世界各国纷纷采取制定人工智能伦理准则、完善法律法规和行业管理等方式来进行人工智能安全治理。在人工智能安全治理中,人工智能安全技术体系具有重要指导意义。具体而言,人工智能安全技术体系是人工智能安全治理的重要组成部分,是落实人工智能伦理规范和法律监管要求的重要支撑,更是人工智能产业健康有序发展的重要保障。然而,在当前阶段,全球范围内人工智能安全框架普遍缺失,安全风险突出且分立,迫切需要对人工智能各生命周期存在的安全风险进行总结与归纳。为解决上述问题,文中提出了涵盖人工智能安全目标、人工智能安全分级能力、人工智能安全技术和管理体系的人工智能安全框架,期待为社会各界提升人工智能安全防护能力提供有益参考。
中图分类号:
| [1]CHEN X,LIU C,LI B,et al.Targeted backdoor attacks on deep learning systems using data poisoning[J].arXiv:1712.05526,2017. [2]PEI K,CAO Y,YANG J,et al.Deepxplore:Automated whitebox testing of deep learning systems[C]//Proceedings of the 26th Symposium on Operating Systems Principles.2017:1-18. [3]360政企安全.AI框架安全依旧堪忧:360 AI安全研究院披露Tensorflow 24个漏洞[EB/OL].http://www.anquanke.com/post/id/218839. [4]ZHANG T,HE Z,LEE R B.Privacy-preserving machine lear-ning through data obfuscation[J].arXiv:1807.01860,2018. [5]XU K,CAO T,SHAH S,et al.Cleaning the null space:A privacy mechanism for predictors[C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.2017:2789-2795. [6]SZEGEDY C,ZAREMBA W,SUTSKEVER I,et al.Intriguing properties of neural networks[J].arXiv:1312.6199,2013. [7]GEIGEL A.Neural network trojan[J].Journal of Computer Security,2013,21(2):191-232. [8]TRAMÈR F,ZHANG F,JUELS A,et al.Stealing machine learning models via prediction apis[C]//25th {USENIX} Security Symposium ({USENIX} Security 16).2016:601-618. [9]SHOKRI R,STRONATI M,SONG C,et al.Membership infe-rence attacks against machine learning models[C]//2017 IEEE Symposium on Security and Privacy (SP).IEEE,2017:3-18. [10]FREDRIKSON M,LANTZ E,JHA S,et al.Privacy in pharmacogenetics:An end-to-end case study of personalized warfarin dosing[C]//23rd {USENIX} Security Symposium ({USENIX} Security 14).2014:17-32. [11]REN K,MENG Q R,YAN S K,et al.Survey of artificial intelligence data security and privacy protection[J].Chinese Journal of Network and Information Security,2021,7(1):1-10. [12]CHEN D,ZHAO H.Data security and privacy protection issues in cloud computing[C]//2012 International Conference on Computer Science and Electronics Engineering.IEEE,2012,1:647-651. [13]ROBERT M L.The Sliding Scale of Cyber Security[EB/OL].http://www.sans.org/reading-room/whitepapers/analyst/mem-bership/36240,2015-8. [14]YEOM S,GIACOMELLI I,FREDRIKSON M,et al.Privacyrisk in machine learning:Analyzing the connection to overfitting[C]//2018 IEEE 31st Computer Security Foundations Symposium (CSF).IEEE,2018:268-282. [15]JAGIELSKI M,OPREA A,BIGGIO B,et al.Manipulating machine learning:Poisoning attacks and countermeasures for regression learning[C]//2018 IEEE Symposium on Security and Privacy (SP).IEEE,2018:19-35. [16]GILPIN L H,BAU D,YUAN B Z,et al.Explaining explanations:An overview of interpretability of machine learning[C]//2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).IEEE,2018:80-89. [17]HUA Y,ZHANG D,GE S.Research progress in the interpre-tability of deep learning models[J].Journal of Cyber Security,2020,5(3):1-12. [18]ZEILER M D,FERGUS R.Visualizing and understanding con-volutional networks[C]//European Conference on Computer Vision.Springer,Cham,2014:818-833. [19]TALVITIE E.Model Regularization for Stable Sample Rollouts[C]//UAI.2014:780-789. [20]DWORK C.Differential privacy:A survey of results[C]//International Conference on Theory and Applications of Models of Computation.Berlin,Heidelberg:Springer,2008:1-19. [21]GENTRY C.A fully homomorphic encryption scheme[M].Stanford:Stanford University,2009. [22]YANG Q,LIU Y,CHEN T,et al.Federated machine learning:Concept and applications[J].ACM Transactions on Intelligent Systems and Technology (TIST),2019,10(2):1-19. [23]MindSpore[OL].http://www.mindspore.cn/security. [24]Porn Producers Offer to Help Hollywood Take Down Deepfake Videos,Janko,Roettgers[OL].http://variety.com/2018/di-gital/news/deepfakes-porn-adult-industry-1202705749/,Aug.2019. [25]MENG D,CHEN H.Magnet:a two-pronged defense against adversarial examples[C]//Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security.2017:135-147. [26]MA X,LI B,WANG Y,et al.Characterizing adversarial sub-spaces using local intrinsic dimensionality[J].arXiv:1801.02613,2018. [27]GU S,YI P,ZHU T,et al.Detecting Adversarial Examples in Deep Neural Networks using Normalizing Filters[C]//11th International Conference on Agents and Artificial Intelligence.2019. [28]CHEN B,CARVALHO W,BARACALDO N,et al.Detectingbackdoor attacks on deep neural networks by activation clustering[J].arXiv:1811.03728,2018. [29]WANG B,YAO Y,SHAN S,et al.Neural cleanse:Identifying and mitigating backdoor attacks in neural networks[C]//2019 IEEE Symposium on Security and Privacy (SP).IEEE,2019:707-723. [30]OH S J,SCHIELE B,FRITZ M.Towards reverse-engineering black-box neural networks[M]//Explainable AI:Interpreting,Explaining and Visualizing Deep Learning.Springer,Cham,2019:121-144. [31]CHEN Z,XIE L,PANG S,et al.MagDR:Mask-guided Detec-tion and Reconstruction for Defending Deepfakes[J].arXiv:2103.14211,2021. [32]ZHAO H,ZHOU W,CHEN D,et al.Multi-attentional deepfake detection[J].arXiv:2103.02406,2021. [33]MADRY A,MAKELOV A,SCHMIDT L,et al.Towards deep learning models resistant to adversarial attacks[J].arXiv:1706.06083,2017. [34]BOSE A,HAMILTON W.Compositional fairness constraintsfor graph embeddings[C]//International Conference on Machine Learning.PMLR,2019:715-724. [35]CHAKRABORTY S,TOMSETT R,RAGHAVENDRA R,et al.Interpretability of deep learning models:a survey of results[C]//2017 IEEE smartworld,Ubiquitous Intelligence & Computing,Advanced & Trusted Computed,Scalable Computing & Communications,Cloud & Big Data Computing,Internet of People and Smart City Innovation (Smartworld/SCALCOM/UIC/ATC/CBDcom/IOP/SCI).IEEE,2017:1-6. [36]LIU X,WANG X,MATWIN S.Improving the interpretability of deep neural networks with knowledge distillation[C]//2018 IEEE International Conference on Data Mining Workshops (ICDMW).IEEE,2018:905-912. [37]YANG C,RANGARAJAN A,RANKA S.Global model interpretation via recursive partitioning[C]//2018 IEEE 20th International Conference on High Performance Computing and Communications;IEEE 16th International Conference on Smart City;IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).IEEE,2018:1563-1570. [38]ZHANG J,CHEN D,LIAO J,et al.Model watermarking forimage processing networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020,34(7):12805-12812. [39]TRAN B,LI J,MADRY A.Spectral signatures in backdoor attacks[J].arXiv:1811.00636,2018. [40]FELDMAN M,FRIEDLER S A,MOELLER J,et al.Certifying and removing disparate impact[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.2015:259-268. [41]BELLAMY R K E,DEY K,HIND M,et al.AI Fairness 360:An extensible toolkit for detecting,understanding,and mitigating unwanted algorithmic bias[J].arXiv:1810.01943,2018. |
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