暂无分享,去创建一个
David A. Forsyth | Jiajun Lu | Hussein Sibai | Evan Fabry | D. Forsyth | Jiajun Lu | Hussein Sibai | Evan Fabry
[1] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[2] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[3] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[4] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[5] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[7] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[8] Seyed-Mohsen Moosavi-Dezfooli,et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Seyed-Mohsen Moosavi-Dezfooli,et al. Robustness of classifiers: from adversarial to random noise , 2016, NIPS.
[10] Patrick D. McDaniel,et al. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples , 2016, ArXiv.
[11] Lujo Bauer,et al. Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition , 2016, CCS.
[12] David A. Wagner,et al. Defensive Distillation is Not Robust to Adversarial Examples , 2016, ArXiv.
[13] David J. Fleet,et al. Adversarial Manipulation of Deep Representations , 2015, ICLR.
[14] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Seyed-Mohsen Moosavi-Dezfooli,et al. Universal Adversarial Perturbations , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Jan Hendrik Metzen,et al. On Detecting Adversarial Perturbations , 2017, ICLR.
[17] David A. Forsyth,et al. SafetyNet: Detecting and Rejecting Adversarial Examples Robustly , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[18] Dawn Xiaodong Song,et al. Delving into Transferable Adversarial Examples and Black-box Attacks , 2016, ICLR.
[19] Mykel J. Kochenderfer,et al. Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks , 2017, CAV.
[20] Samy Bengio,et al. Adversarial examples in the physical world , 2016, ICLR.
[21] Pascal Frossard,et al. Analysis of classifiers’ robustness to adversarial perturbations , 2015, Machine Learning.