Page 147 - 《软件学报》2020年第9期

2768 Journal of Software 软件学报 Vol.31, No.9, September 2020

[4] Rodrigues JD, Rebouças Filho PP, Peixoto Jr E, Kumar A, de Albuquerque VH. Classification of EEG signals to detect alcoholism
using machine learning techniques. Pattern Recognition Letters, 2019,125:140−149.
[5] Zhang Y, Li PS, Wang XH. Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access,
2019,7:31711−31722.
[6] Athalye A, Carlini N, Wagner D. Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial
examples. In: Proc. of the Int’l Conf. on Machine Learning (ICML). 2018. 274−283.
[7] Lu J, Issaranon T, Forsyth D. Safetynet: Detecting and rejecting adversarial examples robustly. In: Proc. of the 2017 IEEE Int’l
Conf. on Computer Vision (ICCV). 2017. 446−454.
[8] Metzen JH, Genewein T, Fischer V, Bischoff B. On detecting adversarial perturbations. In: Proc. of Int’l Conf. on Learning
Representations (ICLR). 2017.
[9] Carlini N, Wagner D. Adversarial examples are not easily detected: Bypassing ten detection methods. In: Proc. of the 10th ACM
Workshop on Artificial Intelligence and Security. 2017. 3−14.
[10] Liao FZ, Liang M, Dong YP, Pang TY, Zhu J, Hu XL. Defense against adversarial attacks using high-level representation guided
denoiser. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 2018. 1778−1787.
[11] Pang TY, Xu K, Du C, Chen N, Zhu J. Improving adversarial robustness via promoting ensemble diversity. In: Proc. of Int’l Conf.
on Machine Learning (ICML). 2019. 4970−4979.
[12] Teerapittayanon S, McDanel B, Kung H. BranchyNet: Fast inference via early exiting from deep neural networks. In: Proc. of the
IEEE Int’l Conf. Pattern Recognition (ICPR). 2016. 2464−2469.
[13] Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S. Feature pyramid networks for object detection. In: Proc. of the IEEE
Conf. on Computer Vision and Pattern Recognition (CVPR). 2017. 936−944.
[14] He KM, Zhang XY, Ren SQ, Sun J. Deep residual learning for image recognition. In: Proc. of the IEEE Conf. on Computer Vision
and Pattern Recognition (CVPR). 2016. 770−778.
[15] Ranjan R, Sankaranarayanan S, Castillo CD, Chellappa R. Improving network robustness against adversarial attacks with compact
convolution. arXiv preprint arXiv:1712.00699, 2017.
[16] Miyato T, Maeda SI, Koyama M, Ishii S. Virtual adversarial training: A regularization method for supervised and semi-supervised
learning. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2018,41(8):1979−1993.
[17] Kurakin A, Goodfellow I, Bengio S. Adversarial machine learning at scale. In: Proc. of the Int’l Conf. on Learning Representations
(ICLR). 2017.
[18] Kurakin A, Goodfellow I, Bengio S, Dong YP, Liao FZ, Liang M, Pang TY,Zhu J, Hu, XL, Xie CH, et al. Adversarial attacks and
defences competition. In: Proc. of the NIPS 2017 Competition: Building Intelligent Systems. Cham: Springer-Verlag, 2018.
195−231.
[19] Samangouei P, Kabkab M, Chellappa R. Defense-Gan: Protecting classifiers against adversarial attacks using generative models. In:
Proc. of the Int’l Conf. on Learning Representations (ICLR). 2018.
[20] Guo C, Rana M, Cisse M, Van Der Maaten L. Countering adversarial images using input transformations. In: Proc. of the Int’l Conf.
on Learning Representations (ICLR). 2018.
[21] Lamb A, Binas J, Goyal A, Serdyuk D, Subramanian S, Mitliagkas I, Bengio Y. Fortified networks: Improving the robustness of
deep networks by modeling the manifold of hidden representations. arXiv preprint arXiv:1804.02485, 2018.
[22] Goodfellow IJ, Shlens J, Szegedy C. Explaining and harnessing adversarial examples. In: Proc. of the Int’l Conf. on Learning
Representations (ICLR). 2015.
[23] Kurakin A, Goodfellow IJ, Bengio S. Adversarial examples in the physical world. In: Proc. of the Int’l Conf. on Learning
Representations (ICLR) Workshop. 2017.
[24] Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A. Towards deep learning models resistant to adversarial attacks. In: Proc. of
the Int’l Conf. on Learning Representations (ICLR). 2018.
[25] Dong YP, Liao FZ, Pang TY, Su H, Hu XL, Li JG, Zhu J. Boosting adversarial attacks with momentum. In: Proc. of the IEEE Conf.
on Computer Vision and Pattern Recognition (CVPR). 2018. 9185−9193.