Untargeted attack on targeted-label for multi-label image classification

The current 3D point cloud feature extraction algorithms are mostly based on geometric features of points. And the distribution of feature points is so messy to accurately locate. This paper proposes a point cloud feature extraction algorithm using 2D-3D transformation. By selecting three pairs of 2D image and 3D point cloud feature points, the conversion matrix of image and point cloud coordinates is calculated to establish a mapping relationship and then we realize the extraction of point cloud features. Experimental results show that compared with other algorithms, the algorithm proposed in this paper can extract the detailed features of point cloud more accurately.

[1]  Grigorios Tsoumakas,et al.  Random k -Labelsets: An Ensemble Method for Multilabel Classification , 2007, ECML.

[2]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[3]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Jonathon Shlens,et al.  Explaining and Harnessing Adversarial Examples , 2014, ICLR.

[5]  Dawn Song,et al.  Physical Adversarial Examples for Object Detectors , 2018, WOOT @ USENIX Security Symposium.

[6]  Xiu-Shen Wei,et al.  Multi-Label Image Recognition With Graph Convolutional Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[8]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Margaret Mitchell,et al.  VQA: Visual Question Answering , 2015, International Journal of Computer Vision.

[10]  Yahong Han,et al.  Curls & Whey: Boosting Black-Box Adversarial Attacks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Samy Bengio,et al.  Adversarial examples in the physical world , 2016, ICLR.

[13]  Amanda Clare,et al.  Knowledge Discovery in Multi-label Phenotype Data , 2001, PKDD.

[14]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[15]  David A. Wagner,et al.  Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).

[16]  Alan L. Yuille,et al.  Adversarial Examples for Semantic Segmentation and Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[17]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Aleksander Madry,et al.  Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.

[19]  Xiaogang Wang,et al.  Deeply learned attributes for crowded scene understanding , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[21]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[22]  Luc Van Gool,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[23]  Zhi-Hua Zhou,et al.  ML-KNN: A lazy learning approach to multi-label learning , 2007, Pattern Recognit..

[24]  Jun Zhu,et al.  Boosting Adversarial Attacks with Momentum , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  Hao Guo,et al.  Visual Attention Consistency Under Image Transforms for Multi-Label Image Classification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[27]  Toon Goedemé,et al.  Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[28]  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).

[29]  Xiao Huang,et al.  Multi-label Adversarial Perturbations , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[30]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[31]  Li Fei-Fei,et al.  Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).