# Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations

arXiv: https://arxiv.org/abs/2501.05588 · HTML: https://mlsanity.com/paper/2501.05588/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Machine Learning in High Energy Physics Community White Paper](https://mlsanity.com/paper/1807.02876/) (2018)
- [An Extensive Data Processing Pipeline for MIMIC-IV](https://mlsanity.com/paper/2204.13841/) (2022)
- [Imperceptible Adversarial Attacks on Tabular Data](https://mlsanity.com/paper/1911.03274/) (2019)
- [Machine Learning for Anomaly Detection in Particle Physics](https://mlsanity.com/paper/2312.14190/) (2023)

## Built on by (most cited first, 5 of 5)

- [MiniFool -- Physics-Constraint-Aware Minimizer-Based Adversarial Attacks in Deep Neural Networks](https://mlsanity.com/paper/2511.01352/) (2025)
- [Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications](https://mlsanity.com/paper/2603.13970/) (2026)
- [Colliding with Adversaries at ECML-PKDD 2025 Adversarial Attack Competition 1st Prize Solution](https://mlsanity.com/paper/2510.16440/) (2025)
- [Colliding with Adversaries at ECML-PKDD 2025 Model Robustness Competition 1st Prize Solution](https://mlsanity.com/paper/2510.16443/) (2025)
- [Uncovering Hidden Systematics in Neural Network Models for High Energy Physics](https://mlsanity.com/paper/2605.07470/) (2026)

## Abstract (arXiv)

Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high energy physics, one of the common deep learning use-cases is the classification of signal and background processes in particle collisions. In many such cases, the fundamental principles of the correlations between observables are often better understood than the actual distributions of the observables themselves. In this work, we present a new adversarial attack algorithm called Random Distribution Shuffle Attack (RDSA), emphasizing the correlations between observables in the network rather than individual feature characteristics. Correct application of the proposed novel attack can result in a significant improvement in classification performance - particularly in the context of data augmentation - when using the generated adversaries within adversarial training. Given that correlations between input features are also crucial in many other disciplines. We demonstrate the RDSA effectiveness on six classification tasks, including two particle collision challenges (using CERN Open Data), hand-written digit recognition (MNIST784), human activity recognition (HAR), weather forecasting (Rain in Australia), and ICU patient mortality (MIMIC-IV), demonstrating a general use case beyond fundamental physics for this new type of adversarial attack algorithms.