Divide, Denoise, and Defend against Adversarial Attacks

19 February 2018

Papers citing "Divide, Denoise, and Defend against Adversarial Attacks"

8 / 8 papers shown

Title
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness Ambar Pal Huaijin Hao Rene Vidal 26 8 0 28 Sep 2023
Group-based Robustness: A General Framework for Customized Robustness in the Real World Weiran Lin Keane Lucas Neo Eyal Lujo Bauer Michael K. Reiter Mahmood Sharif OOD AAML 42 1 0 29 Jun 2023
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks Weiran Lin Keane Lucas Lujo Bauer Michael K. Reiter Mahmood Sharif AAML 31 5 0 28 Dec 2021
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 33 156 0 08 Sep 2020
Class-Aware Domain Adaptation for Improving Adversarial Robustness Xianxu Hou Jingxin Liu Bolei Xu Xiaolong Wang Bozhi Liu Guoping Qiu OOD AAML 43 8 0 10 May 2020
A unified view on differential privacy and robustness to adversarial examples Rafael Pinot Florian Yger Cédric Gouy-Pailler Jamal Atif AAML 21 17 0 19 Jun 2019
Structured Adversarial Attack: Towards General Implementation and Better Interpretability Kaidi Xu Sijia Liu Pu Zhao Pin-Yu Chen Huan Zhang Quanfu Fan Deniz Erdogmus Yanzhi Wang X. Lin AAML 18 160 0 05 Aug 2018
Pixel Recurrent Neural Networks Aaron van den Oord Nal Kalchbrenner Koray Kavukcuoglu SSeg GAN 272 2,552 0 25 Jan 2016