Adversarial Vision Challenge
Not on the map. Why?
Abstract (arXiv)
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural Information Processing Systems (NIPS 2018).
Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). First posted on arXiv 6 Aug 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 1; 2023: 4; 2024: 1.
9 papers.
- 2023Better Diffusion Models Further Improve Adversarial Trainingcited by 208
- 2021Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removalcited by 16
- 2021Learning-to-learn non-convex piecewise-Lipschitz functionscited by 15
- 2022Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systemscited by 5
- 2023Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Studycited by 5
- 2023Enhance Diffusion to Improve Robust Generalizationcited by 3
- 2023Selecting Models based on the Risk of Damage Caused by Adversarial Attackscited by 1
- 2021Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustnesscited by 0
- 2024Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscapecited by 0
- 2024Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape9 Mar 2024
- 2023Enhance Diffusion to Improve Robust Generalization5 Jun 2023
- 2023Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Study27 Feb 2023
- 2023Better Diffusion Models Further Improve Adversarial Training9 Feb 2023
- 2023Selecting Models based on the Risk of Damage Caused by Adversarial Attacks28 Jan 2023
- 2022Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems7 Oct 2022
- 2021Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal7 Dec 2021
- 2021Learning-to-learn non-convex piecewise-Lipschitz functions19 Aug 2021
- 2021Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness27 Mar 2021