# A Novel Noise Injection-based Training Scheme for Better Model Robustness

arXiv: https://arxiv.org/abs/2302.10802 · HTML: https://mlsanity.com/paper/2302.10802/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 58 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 28 of 28)

- [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)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Delving into Transferable Adversarial Examples and Black-box Attacks](https://mlsanity.com/paper/1611.02770/) (2016)
- [Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models](https://mlsanity.com/paper/1712.04248/) (2017)
- [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://mlsanity.com/paper/1904.02884/) (2019)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [Physical Adversarial Examples for Object Detectors](https://mlsanity.com/paper/1807.07769/) (2018)
- [Difference Target Propagation](https://mlsanity.com/paper/1412.7525/) (2014)
- [Transferable Adversarial Attacks for Image and Video Object Detection](https://mlsanity.com/paper/1811.12641/) (2018)
- [QEBA: Query-Efficient Boundary-Based Blackbox Attack](https://mlsanity.com/paper/2005.14137/) (2020)
- [Triangle Attack: A Query-efficient Decision-based Adversarial Attack](https://mlsanity.com/paper/2112.06569/) (2021)
- [Biologically Motivated Algorithms for Propagating Local Target Representations](https://mlsanity.com/paper/1805.11703/) (2018)
- [Robustness of Neural Networks: A Probabilistic and Practical Approach](https://mlsanity.com/paper/1902.05983/) (2019)
- [Query-efficient Meta Attack to Deep Neural Networks](https://mlsanity.com/paper/1906.02398/) (2019)
- [Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems](https://mlsanity.com/paper/2003.02301/) (2020)
- [Adversarial Noise Layer: Regularize Neural Network By Adding Noise](https://mlsanity.com/paper/1805.08000/) (2018)
- [Spiking Neural Predictive Coding for Continual Learning from Data Streams](https://mlsanity.com/paper/1908.08655/) (2019)
- [Yet another but more efficient black-box adversarial attack: tiling and evolution strategies](https://mlsanity.com/paper/1910.02244/) (2019)
- [Noise Optimization for Artificial Neural Networks](https://mlsanity.com/paper/2102.04450/) (2021)
- [A Modeled Approach for Online Adversarial Test of Operational Vehicle Safety (extended version)](https://mlsanity.com/paper/2009.12222/) (2020)
- [Self-Adaptive Soft Voice Activity Detection using Deep Neural Networks for Robust Speaker Verification](https://mlsanity.com/paper/1909.11886/) (2019)

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

- [Learning to Transform Dynamically for Better Adversarial Transferability](https://mlsanity.com/paper/2405.14077/) (2024)
- [Noisy Spiking Actor Network for Exploration](https://mlsanity.com/paper/2403.04162/) (2024)
- [Interleaved Noise Injection Improves Clean, Corrupted, and OOD Performance](https://mlsanity.com/paper/2607.14466/) (2026)

## Abstract (arXiv)

Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work. In this work, we propose a novel noise injection-based training scheme for better model robustness. Specifically, we first develop a likelihood ratio method to estimate the gradient with respect to both synaptic weights and noise levels for stochastic gradient descent training. Then, we design an approximation for the vanilla noise injection-based training method to reduce memory and improve computational efficiency. Next, we apply our proposed scheme to spiking neural networks and evaluate the performance of classification accuracy and robustness on MNIST and Fashion-MNIST datasets. Experiment results show that our proposed method achieves a much better performance on adversarial robustness and slightly better performance on original accuracy, compared with the conventional gradient-based training method.