Universal Adversarial Perturbation via Prior Driven Uncertainty Approximation
暂无分享,去创建一个
Jie Li | Rongrong Ji | Yongjian Wu | Feiyue Huang | Baochang Zhang | Hong Liu | Yue Gao | Yue Gao | Feiyue Huang | Rongrong Ji | Baochang Zhang | Yongjian Wu | Hong Liu | Jie Li
[1] Yarin Gal,et al. Understanding Measures of Uncertainty for Adversarial Example Detection , 2018, UAI.
[2] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[3] Ajmal Mian,et al. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.
[4] Matthias Bethge,et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.
[5] Yarin Gal,et al. Uncertainty in Deep Learning , 2016 .
[6] Seyed-Mohsen Moosavi-Dezfooli,et al. Universal Adversarial Perturbations , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] R. Venkatesh Babu,et al. Generalizable Data-Free Objective for Crafting Universal Adversarial Perturbations , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[9] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[10] Richard Shin. JPEG-resistant Adversarial Images , 2017 .
[11] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[12] Jun Zhu,et al. Boosting Adversarial Attacks with Momentum , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[13] Yarin Gal,et al. Dropout Inference in Bayesian Neural Networks with Alpha-divergences , 2017, ICML.
[14] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[15] Samy Bengio,et al. Adversarial examples in the physical world , 2016, ICLR.
[16] Valentin Khrulkov,et al. Art of Singular Vectors and Universal Adversarial Perturbations , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[17] Bolei Zhou,et al. Interpretable Basis Decomposition for Visual Explanation , 2018, ECCV.
[18] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Wenguan Wang,et al. Shifting More Attention to Video Salient Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] R. Venkatesh Babu,et al. NAG: Network for Adversary Generation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[21] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[22] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[23] Ananthram Swami,et al. Practical Black-Box Attacks against Machine Learning , 2016, AsiaCCS.
[24] K. Makarychev. Perturbations , Optimization , and Statistics , 2017 .
[25] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[27] 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).
[28] A. Kiureghian,et al. Aleatory or epistemic? Does it matter? , 2009 .
[29] Dawn Xiaodong Song,et al. Delving into Transferable Adversarial Examples and Black-box Attacks , 2016, ICLR.
[30] R. Venkatesh Babu,et al. Fast Feature Fool: A data independent approach to universal adversarial perturbations , 2017, BMVC.
[31] Hong Liu,et al. Universal Perturbation Attack Against Image Retrieval , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[32] Richard Szeliski,et al. Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.
[33] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Leon A. Gatys,et al. Texture Synthesis Using Convolutional Neural Networks , 2015, NIPS.