Cross-Domain Transferability of Adversarial Perturbations
暂无分享,去创建一个
Fahad Shahbaz Khan | Fatih Murat Porikli | Muhammad Haris Khan | Salman H. Khan | M. H. Khan | Muzammal Naseer | F. Khan | F. Porikli | Muzammal Naseer | Salman Hameed Khan
[1] Yang Song,et al. Constructing Unrestricted Adversarial Examples with Generative Models , 2018, NeurIPS.
[2] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[3] Ian S. Fischer,et al. Adversarial Transformation Networks: Learning to Generate Adversarial Examples , 2017, ArXiv.
[4] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[5] Patrick D. McDaniel,et al. Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples , 2016, ArXiv.
[6] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[7] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] 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).
[9] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Seyed-Mohsen Moosavi-Dezfooli,et al. Universal Adversarial Perturbations , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[12] Alexia Jolicoeur-Martineau,et al. The relativistic discriminator: a key element missing from standard GAN , 2018, ICLR.
[13] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.
[14] Pascal Frossard,et al. Analysis of classifiers’ robustness to adversarial perturbations , 2015, Machine Learning.
[15] Song Bai,et al. Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses , 2019, ECCV.
[16] Jun Zhu,et al. Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] 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).
[18] Dawn Xiaodong Song,et al. Delving into Transferable Adversarial Examples and Black-box Attacks , 2016, ICLR.
[19] Mingyan Liu,et al. Generating Adversarial Examples with Adversarial Networks , 2018, IJCAI.
[20] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[21] Seyed-Mohsen Moosavi-Dezfooli,et al. Robustness of classifiers: from adversarial to random noise , 2016, NIPS.
[22] Alan L. Yuille,et al. Improving Transferability of Adversarial Examples With Input Diversity , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[24] R. Venkatesh Babu,et al. Fast Feature Fool: A data independent approach to universal adversarial perturbations , 2017, BMVC.
[25] Andrew Y. Ng,et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.
[26] Dan Boneh,et al. The Space of Transferable Adversarial Examples , 2017, ArXiv.
[27] Matthias Bethge,et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.
[28] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Isay Katsman,et al. Generative Adversarial Perturbations , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[30] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[31] R. Venkatesh Babu,et al. Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions , 2018, ECCV.
[32] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[33] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.