# Nonideality-aware training makes memristive networks more robust to adversarial attacks

arXiv: https://arxiv.org/abs/2409.19671 · HTML: https://mlsanity.com/paper/2409.19671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack](https://mlsanity.com/paper/1811.09310/) (2018)
- [4K-Memristor Analog-Grade Passive Crossbar Circuit](https://mlsanity.com/paper/1906.12045/) (2019)
- [Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks](https://mlsanity.com/paper/2008.11298/) (2020)

## Abstract (arXiv)

Neural networks are now deployed in a wide number of areas from object classification to natural language systems. Implementations using analog devices like memristors promise better power efficiency, potentially bringing these applications to a greater number of environments. However, such systems suffer from more frequent device faults and overall, their exposure to adversarial attacks has not been studied extensively. In this work, we investigate how nonideality-aware training - a common technique to deal with physical nonidealities - affects adversarial robustness. We find that adversarial robustness is significantly improved, even with limited knowledge of what nonidealities will be encountered during test time.