# Architectural Adversarial Robustness: The Case for Deep Pursuit

arXiv: https://arxiv.org/abs/2011.14427 · HTML: https://mlsanity.com/paper/2011.14427/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2020.

## Built on by (most cited first, 18 of 18)

- [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)
- [Revisiting Sparse Convolutional Model for Visual Recognition](https://mlsanity.com/paper/2210.12945/) (2022)
- [Learning Diverse-Structured Networks for Adversarial Robustness](https://mlsanity.com/paper/2102.01886/) (2021)
- [On the Vulnerability of Skip Connections to Model Inversion Attacks](https://mlsanity.com/paper/2409.01696/) (2024)
- [Perturbation Inactivation Based Adversarial Defense for Face Recognition](https://mlsanity.com/paper/2207.06035/) (2022)
- [Deciphering the Projection Head: Representation Evaluation Self-supervised Learning](https://mlsanity.com/paper/2301.12189/) (2023)
- [Bi-fidelity Evolutionary Multiobjective Search for Adversarially Robust Deep Neural Architectures](https://mlsanity.com/paper/2207.05321/) (2022)
- [Artificial Immune System of Secure Face Recognition Against Adversarial Attacks](https://mlsanity.com/paper/2406.18144/) (2024)
- [Reframing Neural Networks: Deep Structure in Overcomplete Representations](https://mlsanity.com/paper/2103.05804/) (2021)
- [Chaos Theory and Adversarial Robustness](https://mlsanity.com/paper/2210.13235/) (2022)
- [Boosting Adversarial Robustness and Generalization with Dictionary Structure](https://mlsanity.com/paper/2502.00834/) (2025)
- [Rethinking the Role of Spatial Mixing](https://mlsanity.com/paper/2503.16760/) (2025)
- [Exploring the Effect of DNN Depth on Adversarial Attacks in Network Intrusion Detection Systems](https://mlsanity.com/paper/2510.19761/) (2025)
- [Structural Extensions of Basis Pursuit: Guarantees on Adversarial Robustness](https://mlsanity.com/paper/2205.08955/) (2022)
- [A Tensor-based Convolutional Neural Network for Small Dataset Classification](https://mlsanity.com/paper/2303.17061/) (2023)
- [A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?](https://mlsanity.com/paper/2605.18666/) (2026)

## Abstract (arXiv)

Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by reframing each layer of a feed-forward network as an approximate solution to a sparse coding problem. Iterative solutions using basis pursuit are theoretically more stable and have improved adversarial robustness. However, cascading layer-wise pursuit implementations suffer from error accumulation in deeper networks. In contrast, our new method of deep pursuit approximates the activations of all layers as a single global optimization problem, allowing us to consider deeper, real-world architectures with skip connections such as residual networks. Experimentally, our approach demonstrates improved robustness to adversarial noise.