# Anti-Bandit Neural Architecture Search for Model Defense

arXiv: https://arxiv.org/abs/2008.00698 · HTML: https://mlsanity.com/paper/2008.00698/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [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)
- [Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective](https://mlsanity.com/paper/2212.11005/) (2022)
- [Taxonomy of Machine Learning Safety: A Survey and Primer](https://mlsanity.com/paper/2106.04823/) (2021)
- [Defenses in Adversarial Machine Learning: A Survey](https://mlsanity.com/paper/2312.08890/) (2023)
- [Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design](https://mlsanity.com/paper/2103.15750/) (2021)
- [Learning Diverse-Structured Networks for Adversarial Robustness](https://mlsanity.com/paper/2102.01886/) (2021)
- [Reconsidering CO2 emissions from Computer Vision](https://mlsanity.com/paper/2104.08702/) (2021)
- [Neural Architecture Search via Combinatorial Multi-Armed Bandit](https://mlsanity.com/paper/2101.00336/) (2021)
- [Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures](https://mlsanity.com/paper/2207.05321/) (2022)
- [Towards Accurate and Robust Architectures via Neural Architecture Search](https://mlsanity.com/paper/2405.05502/) (2024)
- [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)
- [Associative Adversarial Learning Based on Selective Attack](https://mlsanity.com/paper/2112.13989/) (2021)
- [Revisiting and Advancing Adversarial Training Through A Simple Baseline](https://mlsanity.com/paper/2306.07613/) (2023)
- [ProARD: progressive adversarial robustness distillation: provide wide range of robust students](https://mlsanity.com/paper/2506.07666/) (2025)

## Abstract (arXiv)

Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks. In this paper, we defend against adversarial attacks using neural architecture search (NAS) which is based on a comprehensive search of denoising blocks, weight-free operations, Gabor filters and convolutions. The resulting anti-bandit NAS (ABanditNAS) incorporates a new operation evaluation measure and search process based on the lower and upper confidence bounds (LCB and UCB). Unlike the conventional bandit algorithm using UCB for evaluation only, we use UCB to abandon arms for search efficiency and LCB for a fair competition between arms. Extensive experiments demonstrate that ABanditNAS is faster than other NAS methods, while achieving an 8.73% improvement over prior arts on CIFAR-10 under PGD-7.