Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation

14 March 2020

Papers citing "Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation"

10 / 10 papers shown

Title
UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation Ye Sun Hao Zhang Tiehua Zhang Xingjun Ma Yu-Gang Jiang VLM 37 3 0 13 Oct 2024
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation Kira Maag Asja Fischer AAML SSeg 39 3 0 26 Oct 2023
Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation Kira Maag Asja Fischer AAML UQCV 23 4 0 22 May 2023
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks Shashank Agnihotri Steffen Jung M. Keuper AAML 37 21 0 04 Feb 2023
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments Xiaogang Xu Hengshuang Zhao Philip Torr Jiaya Jia AAML 32 2 0 11 Dec 2022
Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs Prashant Pandey Aleti Vardhan Mustafa Chasmai Tanuj Sur Brejesh Lall AAML 27 9 0 07 Oct 2022
Adversarial Examples on Segmentation Models Can be Easy to Transfer Jindong Gu Hengshuang Zhao Volker Tresp Philip Torr AAML 41 14 0 22 Nov 2021
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples Xiaojun Jia Xingxing Wei Xiaochun Cao H. Foroosh AAML 69 264 0 30 Nov 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 296 3,112 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 293 5,842 0 08 Jul 2016