暂无分享,去创建一个
[1] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[2] Fabio Roli,et al. Evasion Attacks against Machine Learning at Test Time , 2013, ECML/PKDD.
[3] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[4] Andrew Zisserman,et al. Deep Face Recognition , 2015, BMVC.
[5] James Philbin,et al. FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[7] Dale Schuurmans,et al. Learning with a Strong Adversary , 2015, ArXiv.
[8] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Shin Ishii,et al. Distributional Smoothing by Virtual Adversarial Examples , 2015, ICLR.
[10] Yu Qiao,et al. A Discriminative Feature Learning Approach for Deep Face Recognition , 2016, ECCV.
[11] Ananthram Swami,et al. Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples , 2016, ArXiv.
[12] Michael P. Wellman,et al. Towards the Science of Security and Privacy in Machine Learning , 2016, ArXiv.
[13] Samy Bengio,et al. Adversarial examples in the physical world , 2016, ICLR.
[14] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[15] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[16] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.