# Robust Assessment of Real-World Adversarial Examples

arXiv: https://arxiv.org/abs/1911.10435 · HTML: https://mlsanity.com/paper/1911.10435/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Group-wise Inhibition based Feature Regularization for Robust Classification](https://mlsanity.com/paper/2103.02152/) (2021)
- [CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models](https://mlsanity.com/paper/2206.04365/) (2022)

## Abstract (arXiv)

We explore rigorous, systematic, and controlled experimental evaluation of adversarial examples in the real world and propose a testing regimen for evaluation of real world adversarial objects. We show that for small scene/ environmental perturbations, large adversarial performance differences exist. Current state of adversarial reporting exists largely as a frequency count over a dynamic collections of scenes. Our work underscores the need for either a more complete report or a score that incorporates scene changes and baseline performance for models and environments tested by adversarial developers. We put forth a score that attempts to address the above issues in a straight-forward exemplar application for multiple generated adversary examples. We contribute the following: 1. a testbed for adversarial assessment, 2. a score for adversarial examples, and 3. a collection of additional evaluations on testbed data.