# Adversarial Vision Challenge

arXiv: https://arxiv.org/abs/1808.01976 · HTML: https://mlsanity.com/paper/1808.01976/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [Better Diffusion Models Further Improve Adversarial Training](https://mlsanity.com/paper/2302.04638/) (2023)
- [Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal](https://mlsanity.com/paper/2112.03492/) (2021)
- [Learning-to-learn non-convex piecewise-Lipschitz functions](https://mlsanity.com/paper/2108.08770/) (2021)
- [Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems](https://mlsanity.com/paper/2210.03297/) (2022)
- [Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Study](https://mlsanity.com/paper/2302.13570/) (2023)
- [Enhance Diffusion to Improve Robust Generalization](https://mlsanity.com/paper/2306.02618/) (2023)
- [Selecting Models based on the Risk of Damage Caused by Adversarial Attacks](https://mlsanity.com/paper/2301.12151/) (2023)
- [Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness](https://mlsanity.com/paper/2103.14795/) (2021)
- [Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape](https://mlsanity.com/paper/2403.06013/) (2024)

## 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).