The art of defense: letting networks fool the attacker

7 April 2021

Papers citing "The art of defense: letting networks fool the attacker"

8 / 8 papers shown

Title
Robust 3D Point Clouds Classification based on Declarative Defenders Kaidong Li Tianxiao Zhang Cuncong Zhong Z. Zhang G. Wang 3DPC 45 1 0 13 Oct 2024
A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense Ryota Iijima Sayaka Shiota Hitoshi Kiya 36 6 0 11 Feb 2024
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation Amira Guesmi Muhammad Abdullah Hanif B. Ouni Muhammad Shafique MDE 40 12 0 06 Aug 2023
Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis Tiange Xiang Chaoyi Zhang Yang Song Jianhui Yu Weidong (Tom) Cai 3DPC 148 283 0 04 May 2021
A New Defense Against Adversarial Images: Turning a Weakness into a Strength Tao Yu Shengyuan Hu Chuan Guo Wei-Lun Chao Kilian Q. Weinberger AAML 55 101 0 16 Oct 2019
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 222 14,103 0 02 Dec 2016
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition Zifeng Wu Chunhua Shen Anton Van Den Hengel SSeg 260 1,491 0 30 Nov 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 264 3,110 0 04 Nov 2016