# Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples

arXiv: https://arxiv.org/abs/1705.10686 · HTML: https://mlsanity.com/paper/1705.10686/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2017.

## Built on by (most cited first, 3 of 3)

- [Security and Privacy for Artificial Intelligence: Opportunities and Challenges](https://mlsanity.com/paper/2102.04661/) (2021)
- [Adversarial Attacks on Image Classification Models: Analysis and Defense](https://mlsanity.com/paper/2312.16880/) (2023)
- [Resilience from Diversity: Population-based approach to harden models against adversarial attacks](https://mlsanity.com/paper/2111.10272/) (2021)

## Abstract (arXiv)

Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results showing that simple feature squeezing techniques also make deep learning models significantly more robust against the Carlini/Wagner attacks, which are the best known adversarial methods discovered to date.