Efficient Joint Gradient Based Attack Against SOR Defense for 3D Point Cloud Classification
暂无分享,去创建一个
Xiaopeng Zhang | Weiliang Meng | Shibiao Xu | Baoyuan Wu | Chengcheng Ma | Xiaopeng Zhang | Shibiao Xu | Chengcheng Ma | Weiliang Meng | Baoyuan Wu
[1] Baoyuan Wu,et al. Toward Adversarial Robustness via Semi-supervised Robust Training , 2020, ArXiv.
[2] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[3] David A. Wagner,et al. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples , 2018, ICML.
[4] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[5] Tsung-Yi Ho,et al. Robust Adversarial Objects against Deep Learning Models , 2020, AAAI.
[6] Bingbing Ni,et al. Adversarial Attack and Defense on Point Sets , 2019, ArXiv.
[7] Baoyuan Wu,et al. Boosting Decision-Based Black-Box Adversarial Attacks with Random Sign Flip , 2020, ECCV.
[8] Kejiang Chen,et al. DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[9] Mitali Bafna,et al. Thwarting Adversarial Examples: An L_0-Robust Sparse Fourier Transform , 2018, NeurIPS.
[10] Baoyuan Wu,et al. Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations , 2019, ArXiv.
[11] Baoyuan Wu,et al. Rethinking the Trigger of Backdoor Attack , 2020, ArXiv.
[12] Takayuki Suzuki,et al. Mixing sauces , 2019, ACM Trans. Graph..
[13] Matthew Wicker,et al. Robustness of 3D Deep Learning in an Adversarial Setting , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Wei Liu,et al. Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Andrew Lim,et al. On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Xiaolin Hu,et al. Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[17] Kui Ren,et al. PointCloud Saliency Maps , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[18] Hao Su,et al. Adversarial point perturbations on 3D objects , 2019, ArXiv.
[19] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[20] Baoyuan Wu,et al. Sparse Adversarial Attack via Perturbation Factorization , 2020, ECCV.
[21] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Baoyuan Wu,et al. Efficient Black-Box Adversarial Attack Guided by the Distribution of Adversarial Perturbations , 2020, ArXiv.
[23] Nico Blodow,et al. Towards 3D Point cloud based object maps for household environments , 2008, Robotics Auton. Syst..
[24] Hang Su,et al. Benchmarking Adversarial Robustness on Image Classification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Hao Su,et al. Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers , 2019, 2019 IEEE International Conference on Image Processing (ICIP).
[26] Chong Xiang,et al. Generating 3D Adversarial Point Clouds , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Daniel Cohen-Or,et al. PU-Net: Point Cloud Upsampling Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[28] Dan Boneh,et al. Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.
[29] Yong Zhang,et al. Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients , 2020, ArXiv.
[30] Moustapha Cissé,et al. Countering Adversarial Images using Input Transformations , 2018, ICLR.
[31] Yue Wang,et al. Dynamic Graph CNN for Learning on Point Clouds , 2018, ACM Trans. Graph..
[32] Logan Engstrom,et al. Synthesizing Robust Adversarial Examples , 2017, ICML.
[33] Baoyuan Wu,et al. Exact Adversarial Attack to Image Captioning via Structured Output Learning With Latent Variables , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Bernard Ghanem,et al. AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds , 2020, ECCV.
[35] J. Zico Kolter,et al. Certified Adversarial Robustness via Randomized Smoothing , 2019, ICML.
[36] Ke Chen,et al. Geometry-aware Generation of Adversarial and Cooperative Point Clouds , 2019, ArXiv.
[37] Leonidas J. Guibas,et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Leonidas J. Guibas,et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space , 2017, NIPS.