# Sparse DNNs with Improved Adversarial Robustness

arXiv: https://arxiv.org/abs/1810.09619 · HTML: https://mlsanity.com/paper/1810.09619/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2018.

## Built on by (most cited first, 30 of 72)

- [The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers](https://mlsanity.com/paper/2210.06313/) (2022)
- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training](https://mlsanity.com/paper/2202.02643/) (2022)
- [Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy](https://mlsanity.com/paper/2103.03014/) (2021)
- [Pruning has a disparate impact on model accuracy](https://mlsanity.com/paper/2205.13574/) (2022)
- [Sparsity Winning Twice: Better Robust Generalization from More Efficient Training](https://mlsanity.com/paper/2202.09844/) (2022)
- [Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks](https://mlsanity.com/paper/2110.14068/) (2021)
- [Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!](https://mlsanity.com/paper/2303.02141/) (2023)
- [Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning](https://mlsanity.com/paper/2401.10862/) (2024)
- [CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection](https://mlsanity.com/paper/2102.05311/) (2021)
- [Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network](https://mlsanity.com/paper/2204.02738/) (2022)
- [Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations](https://mlsanity.com/paper/2203.06514/) (2022)
- [Towards Sparsification of Graph Neural Networks](https://mlsanity.com/paper/2209.04766/) (2022)
- [Sparse maximal update parameterization: A holistic approach to sparse training dynamics](https://mlsanity.com/paper/2405.15743/) (2024)
- [Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation](https://mlsanity.com/paper/2106.07849/) (2021)
- [Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free](https://mlsanity.com/paper/2205.11819/) (2022)
- [Temporal Coherent Test-Time Optimization for Robust Video Classification](https://mlsanity.com/paper/2302.14309/) (2023)
- [Pruning Adversarially Robust Neural Networks without Adversarial Examples](https://mlsanity.com/paper/2210.04311/) (2022)
- [How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts](https://mlsanity.com/paper/2207.01168/) (2022)
- [Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness](https://mlsanity.com/paper/2409.01249/) (2024)
- [Recent Advances in Large Margin Learning](https://mlsanity.com/paper/2103.13598/) (2021)
- [Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence](https://mlsanity.com/paper/2311.15782/) (2023)
- [Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study](https://mlsanity.com/paper/2405.20876/) (2024)
- [Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis](https://mlsanity.com/paper/2103.00778/) (2021)
- [Pruning in the Face of Adversaries](https://mlsanity.com/paper/2108.08560/) (2021)
- [CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness](https://mlsanity.com/paper/2212.01957/) (2022)
- [Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers](https://mlsanity.com/paper/2509.05086/) (2025)
- [The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks](https://mlsanity.com/paper/2104.06153/) (2021)
- [Visual Attention Emerges from Recurrent Sparse Reconstruction](https://mlsanity.com/paper/2204.10962/) (2022)
- [Wavelet Regularization Benefits Adversarial Training](https://mlsanity.com/paper/2206.03727/) (2022)

## Abstract (arXiv)

Deep neural networks (DNNs) are computationally/memory-intensive and vulnerable to adversarial attacks, making them prohibitive in some real-world applications. By converting dense models into sparse ones, pruning appears to be a promising solution to reducing the computation/memory cost. This paper studies classification models, especially DNN-based ones, to demonstrate that there exists intrinsic relationships between their sparsity and adversarial robustness. Our analyses reveal, both theoretically and empirically, that nonlinear DNN-based classifiers behave differently under l2 attacks from some linear ones. We further demonstrate that an appropriately higher model sparsity implies better robustness of nonlinear DNNs, whereas over-sparsified models can be more difficult to resist adversarial examples.