# A Statistical Method for Attack-Agnostic Adversarial Attack Detection with Compressive Sensing Comparison

arXiv: https://arxiv.org/abs/2510.02707 · HTML: https://mlsanity.com/paper/2510.02707/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 19 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks](https://mlsanity.com/paper/1704.01155/) (2017)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors](https://mlsanity.com/paper/1909.06872/) (2019)
- [LiBRe: A Practical Bayesian Approach to Adversarial Detection](https://mlsanity.com/paper/2103.14835/) (2021)
- [The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples](https://mlsanity.com/paper/1803.10418/) (2018)
- [Attack Agnostic Statistical Method for Adversarial Detection](https://mlsanity.com/paper/1911.10008/) (2019)
- [Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis](https://mlsanity.com/paper/2012.06405/) (2020)
- [Attack-Agnostic Adversarial Detection](https://mlsanity.com/paper/2206.00489/) (2022)

## Abstract (arXiv)

Adversarial attacks present a significant threat to modern machine learning systems. Yet, existing detection methods often lack the ability to detect unseen attacks or detect different attack types with a high level of accuracy. In this work, we propose a statistical approach that establishes a detection baseline before a neural network's deployment, enabling effective real-time adversarial detection. We generate a metric of adversarial presence by comparing the behavior of a compressed/uncompressed neural network pair. Our method has been tested against state-of-the-art techniques, and it achieves near-perfect detection across a wide range of attack types. Moreover, it significantly reduces false positives, making it both reliable and practical for real-world applications.