# DSRNA: Differentiable Search of Robust Neural Architectures

arXiv: https://arxiv.org/abs/2012.06122 · HTML: https://mlsanity.com/paper/2012.06122/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 27 of 27)

- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks](https://mlsanity.com/paper/2110.03825/) (2021)
- [Neural Architecture Design and Robustness: A Dataset](https://mlsanity.com/paper/2306.06712/) (2023)
- [Defenses in Adversarial Machine Learning: A Survey](https://mlsanity.com/paper/2312.08890/) (2023)
- [Learning Diverse-Structured Networks for Adversarial Robustness](https://mlsanity.com/paper/2102.01886/) (2021)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [Adversarially Robust Neural Architecture Search for Graph Neural Networks](https://mlsanity.com/paper/2304.04168/) (2023)
- [RobArch: Designing Robust Architectures against Adversarial Attacks](https://mlsanity.com/paper/2301.03110/) (2023)
- [Robust NAS under adversarial training: benchmark, theory, and beyond](https://mlsanity.com/paper/2403.13134/) (2024)
- [Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures](https://mlsanity.com/paper/2207.05321/) (2022)
- [GeNAS: Neural Architecture Search with Better Generalization](https://mlsanity.com/paper/2305.08611/) (2023)
- [Towards Accurate and Robust Architectures via Neural Architecture Search](https://mlsanity.com/paper/2405.05502/) (2024)
- [Exploiting the Potential of Datasets: A Data-Centric Approach for Model Robustness](https://mlsanity.com/paper/2203.05323/) (2022)
- [DASS: Differentiable Architecture Search for Sparse neural networks](https://mlsanity.com/paper/2207.06968/) (2022)
- [Differentiable Search of Accurate and Robust Architectures](https://mlsanity.com/paper/2212.14049/) (2022)
- [Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation](https://mlsanity.com/paper/2305.07308/) (2023)
- [TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model](https://mlsanity.com/paper/2201.02941/) (2022)
- [AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning](https://mlsanity.com/paper/2203.14169/) (2022)
- [SeqNet: An Efficient Neural Network for Automatic Malware Detection](https://mlsanity.com/paper/2205.03850/) (2022)
- [An Evaluation of Zero-Cost Proxies -- from Neural Architecture Performance to Model Robustness](https://mlsanity.com/paper/2307.09365/) (2023)
- [Provably Safeguarding a Classifier from OOD and Adversarial Samples: an Extreme Value Theory Approach](https://mlsanity.com/paper/2501.10202/) (2025)
- [Differentiable Architecture Search for Adversarially Robust Quantum Computer Vision](https://mlsanity.com/paper/2601.18058/) (2026)
- [Adversarially Robust Medical Classification via Attentive Convolutional Neural Networks](https://mlsanity.com/paper/2210.14405/) (2022)
- [Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy](https://mlsanity.com/paper/2304.00486/) (2023)
- [Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization](https://mlsanity.com/paper/2402.18128/) (2024)
- [Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights](https://mlsanity.com/paper/2501.03782/) (2025)
- [ProARD: progressive adversarial robustness distillation: provide wide range of robust students](https://mlsanity.com/paper/2506.07666/) (2025)

## Abstract (arXiv)

In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neural architectures automatically. However, these searched architectures are prone to adversarial attacks. A small perturbation of the input data can render the architecture to change prediction outcomes significantly. To address this problem, we propose methods to perform differentiable search of robust neural architectures. In our methods, two differentiable metrics are defined to measure architectures' robustness, based on certified lower bound and Jacobian norm bound. Then we search for robust architectures by maximizing the robustness metrics. Different from previous approaches which aim to improve architectures' robustness in an implicit way: performing adversarial training and injecting random noise, our methods explicitly and directly maximize robustness metrics to harvest robust architectures. On CIFAR-10, ImageNet, and MNIST, we perform game-based evaluation and verification-based evaluation on the robustness of our methods. The experimental results show that our methods 1) are more robust to various norm-bound attacks than several robust NAS baselines; 2) are more accurate than baselines when there are no attacks; 3) have significantly higher certified lower bounds than baselines.