Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers

16 October 2018

Papers citing "Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers"

12 / 12 papers shown

Title
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 249 3,195 0 01 Feb 2018
Adversarial Patch Tom B. Brown Dandelion Mané Aurko Roy Martín Abadi Justin Gilmer AAML 96 1,097 0 27 Dec 2017
One pixel attack for fooling deep neural networks Jiawei Su Danilo Vasconcellos Vargas Kouichi Sakurai AAML 166 2,327 0 24 Oct 2017
Robust Physical-World Attacks on Deep Learning Models Kevin Eykholt Ivan Evtimov Earlence Fernandes Yue Liu Amir Rahmati Chaowei Xiao Atul Prakash Tadayoshi Kohno Basel Alomair AAML 107 595 0 27 Jul 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 111 894 0 01 Mar 2017
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas Guibas 3DH 3DPC 3DV PINN 508 14,384 0 02 Dec 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 551 5,914 0 08 Jul 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 116 1,742 0 24 May 2016
Improving the Robustness of Deep Neural Networks via Stability Training Stephan Zheng Yang Song Thomas Leung Ian Goodfellow OOD 58 639 0 15 Apr 2016
Spatial Transformer Networks Max Jaderberg Karen Simonyan Andrew Zisserman Koray Kavukcuoglu 318 7,401 0 05 Jun 2015
Transformation Properties of Learned Visual Representations Taco S. Cohen Max Welling 75 106 0 24 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 297 14,978 1 21 Dec 2013