Human Imperceptible Attacks and Applications to Improve Fairness

30 November 2021

Papers citing "Human Imperceptible Attacks and Applications to Improve Fairness"

7 / 7 papers shown

Title
Wasserstein distributional robustness of neural networks Xingjian Bai Guangyi He Yifan Jiang J. Obłój OOD AAML 8 6 0 16 Jun 2023
How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysis Bishwamittra Ghosh D. Basu Kuldeep S. Meel FaML 4 9 0 01 Jun 2022
Image Quality Assessment for Perceptual Image Restoration: A New Dataset, Benchmark and Metric Jinjin Gu Haoming Cai Haoyu Chen Xiaoxing Ye Jimmy S. J. Ren Chao Dong 26 42 0 30 Nov 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 294 4,203 0 23 Aug 2019
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression Nilaksh Das Madhuri Shanbhogue Shang-Tse Chen Fred Hohman Siwei Li Li-Wei Chen Michael E. Kounavis Duen Horng Chau FedML AAML 38 224 0 19 Feb 2018
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 250 5,833 0 08 Jul 2016
Sample Out-Of-Sample Inference Based on Wasserstein Distance Jose H. Blanchet Yang Kang 23 35 0 04 May 2016