# Evolving Robust Neural Architectures to Defend from Adversarial Attacks

arXiv: https://arxiv.org/abs/1906.11667 · HTML: https://mlsanity.com/paper/1906.11667/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 13 of 13)

- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Learning Diverse-Structured Networks for Adversarial Robustness](https://mlsanity.com/paper/2102.01886/) (2021)
- [Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks](https://mlsanity.com/paper/2101.06507/) (2021)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks](https://mlsanity.com/paper/2210.05276/) (2022)
- [Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures](https://mlsanity.com/paper/2207.05321/) (2022)
- [Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation](https://mlsanity.com/paper/2305.07308/) (2023)
- [Evolving Architectures with Gradient Misalignment toward Low Adversarial Transferability](https://mlsanity.com/paper/2109.05919/) (2021)
- [Poisoning the Search Space in Neural Architecture Search](https://mlsanity.com/paper/2106.14406/) (2021)
- [Associative Adversarial Learning Based on Selective Attack](https://mlsanity.com/paper/2112.13989/) (2021)
- [Adversarial Robustness Assessment of NeuroEvolution Approaches](https://mlsanity.com/paper/2207.05451/) (2022)
- [MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models](https://mlsanity.com/paper/2211.10921/) (2022)

## Abstract (arXiv)

Neural networks are prone to misclassify slightly modified input images. Recently, many defences have been proposed, but none have improved the robustness of neural networks consistently. Here, we propose to use adversarial attacks as a function evaluation to search for neural architectures that can resist such attacks automatically. Experiments on neural architecture search algorithms from the literature show that although accurate, they are not able to find robust architectures. A significant reason for this lies in their limited search space. By creating a novel neural architecture search with options for dense layers to connect with convolution layers and vice-versa as well as the addition of concatenation layers in the search, we were able to evolve an architecture that is inherently accurate on adversarial samples. Interestingly, this inherent robustness of the evolved architecture rivals state-of-the-art defences such as adversarial training while being trained only on the non-adversarial samples. Moreover, the evolved architecture makes use of some peculiar traits which might be useful for developing even more robust ones. Thus, the results here confirm that more robust architectures exist as well as opens up a new realm of feasibilities for the development and exploration of neural networks. Code available at http://bit.ly/RobustArchitectureSearch.