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2800 Journal of Software 软件学报 Vol.31, No.9, September 2020
[22] Su JW, Vargas DV, Sakurai K. One pixel attack for fooling deep neural networks. IEEE Trans. on Evolutionary Computation, 2019,
23(5):828−841. [doi: 10.1109/TEVC.2019.2890858]
[23] Brendel W, Rauber J, Bethge M. Decision-Based adversarial attacks: Reliable attacks against black-box machine learning models.
arXiv preprint arXiv:1712.04248, 2017.
[24] Chen PY, Zhang H, Sharma Y, Yi JF, Hsieh CJ. Zoo: Zeroth order optimization based black-box attacks to deep neural networks
without training substitute models. In: Thuraisingham B, ed. Proc. of the 10th ACM Workshop on Artificial Intelligence and
Security (AISec 2017). New York: ACM, 2017. 15−26. [doi: 10.1145/3128572.3140448]
[25] Chen JY, Su MM, Shen SJ, Xiong H, Zheng HB. POBA-GA: Perturbation optimized black-box adversarial attacks via genetic
algorithm. arXiv preprint arXiv:1906.03181, 2019. [doi: 10.1016/j.cose.2019.04.014]
[26] Kurakin A, Goodfellow I, Bengio S. Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533, 2016.
[27] Sharif M, Bhagavatula S, Bauer L, Reiter MK. Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition.
In: Weippl E, ed. Proc. of the ACM Sigsac Conf. on Computer & Communications Security. New York: ACM, 2016. 1528−1540.
[doi: 10.1145/2976749.2978392]
[28] Ma YK, Wu LF, Jian M, Liu FH, Yang Z. Approach to generate adversarial examples for face-spoofing detection. Ruan Jian Xue
Bao/Journal of Software, 2019,30(2):469−480 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5568.htm [doi:
10.13328/j.cnki.jos.005568]
[29] Papernot N, McDaniel P, Jha S, Fredrikson M, Celik ZB, Swami A. The limitations of deep learning in adversarial settings. In:
Zeller A, ed. Proc. of the 2016 IEEE European Symp. on Security and Privacy (EuroS&P). Saarbrucken: IEEE, 2016. 372−387. [doi:
10.1109/EuroSP.2016.36]
[30] Cisse M, Adi Y, Neverova N, Keshet J. Houdini: Fooling deep structured prediction models. arXiv preprint arXiv:1707.05373,
2017.
[31] Dong YP, Liao FZ, Pang TY, Su H, Zhu J, Hu XL, Li JG. Boosting adversarial attacks with momentum. In: Brown M, ed. Proc. of
the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018. 9185−9193.
[32] Sarkar S, Bansal A, Mahbub U, Chellappa R. UPSET and ANGRI: Breaking high performance image classifiers. arXiv preprint
arXiv:1707.01159, 2017.
[33] Dorigo M, Stützle T. Ant colony optimization: Overview and recent advances. Handbook of Metaheuristics, 2010,146(5): 227−263.
[34] Kennedy J, Eberhart R. Particle swarm optimization. In: Si J, ed. Proc. of the Int’l Conf. on Neural Networks (ICNN’95). Perth:
IEEE, 1995. 1942−1948. [doi: 10.1109/ICNN.1995.488968]
[35] Li XL, Shao ZJ, Qian JX. An optimizing method based on autonomous animats: Fish-swarm algorithm. Systems Engineering—
Theory & Practice, 2002,22(11):32−38 (in Chinese with English abstract).
[36] Jiang JG, Zhou JW, Zheng YC, Zhou RS. A double flora bacteria foraging optimization algorithm. Journal of Shenzhen University
Science and Engineering, 2014,31(1):43−51.
[37] Karaboga D. Artificial bee colony algorithm. Scholarpedia, 2010,5(3):6915. [doi: 10.4249 / scholarpedia.6915]
[38] Eusuff MM, Lansey KE. Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of
Water Resources Planning & Management, 2003,129(3):210−225. [doi: 10.1061/(ASCE)0733-9496(2003)129:3(210)]
附中文参考文献:
[4] 卿斯汉.Android 安全研究进展.软件学报,2016,27(1):45−71. http://www.jos.org.cn/1000-9825/4914.htm [doi: 10.13328/j.cnki.jos.
004914]
[11] 包仁达,庾涵,朱德发,黄少飞,孙瑶,刘偲.基于区域敏感生成对抗网络的自动上妆算法.软件学报,2019,30(4):896−913. http://www.
jos.org.cn/1000-9825/5666.htm [doi: 10.13328/j.cnki.jos.005666]
[12] 万波,王泉,高有行.图像分割的误差分散半调算法.西安电子科技大学学报(自然科学版),2009,36(3):496−546.
[13] 王泉,董宝鸳,田玉敏.一种 MPEG-4 视频流的运动目标检测算法.西安电子科技大学学报,2007,34(6):869−872.
[28] 马玉琨,毋立芳,简萌,刘方昊,杨洲.一种面向人脸活体检测的对抗样本生成算法.软件学报,2019,30(2):469−480. http://www.jos.
org.cn/1000-9825/5568.htm [doi: 10.13328/j.cnki.jos.005568]
[35] 李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法.系统工程理论与实践,2002,22(11):32−38.