Defensive Dropout for Hardening Deep Neural Networks under Adversarial
Attacks

Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks

13 September 2018

Papers citing "Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks"

9 / 9 papers shown

Title
Sparta: Spatially Attentive and Adversarially Robust Activation Qing Guo Felix Juefei Xu Changqing Zhou Wei Feng Yang Liu Song Wang AAML 33 4 0 18 May 2021
Deep Video Inpainting Detection Peng Zhou Ning Yu Zuxuan Wu L. Davis Abhinav Shrivastava Ser-Nam Lim 30 18 0 26 Jan 2021
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training Alfred Laugros A. Caplier Matthieu Ospici AAML 24 19 0 19 Aug 2020
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness Pu Zhao Pin-Yu Chen Payel Das Karthikeyan N. Ramamurthy Xue Lin AAML 53 184 0 30 Apr 2020
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses Tianlin Li Siyue Wang Pin-Yu Chen Yanzhi Wang Brian Kulis Xue Lin S. Chin AAML 16 42 0 20 Aug 2019
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method Pu Zhao Sijia Liu Pin-Yu Chen Nghia Hoang Kaidi Xu B. Kailkhura Xue Lin AAML 27 54 0 26 Jul 2019
Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples Huangyi Ge Sze Yiu Chau Bruno Ribeiro Ninghui Li AAML 27 1 0 05 Dec 2018
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 308 5,842 0 08 Jul 2016
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 266 7,638 0 03 Jul 2012