# Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

arXiv: https://arxiv.org/abs/2010.09624 · HTML: https://mlsanity.com/paper/2010.09624/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 28 of 28)

- [Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models](https://mlsanity.com/paper/2305.12827/) (2023)
- [Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks](https://mlsanity.com/paper/2207.13243/) (2022)
- [Adversarial Example Detection for DNN Models: A Review and Experimental Comparison](https://mlsanity.com/paper/2105.00203/) (2021)
- [On the benefits of knowledge distillation for adversarial robustness](https://mlsanity.com/paper/2203.07159/) (2022)
- [Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense](https://mlsanity.com/paper/2209.04779/) (2022)
- [Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic](https://mlsanity.com/paper/2407.07089/) (2024)
- [Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems](https://mlsanity.com/paper/2303.15845/) (2023)
- [Local Competition and Stochasticity for Adversarial Robustness in Deep Learning](https://mlsanity.com/paper/2101.01121/) (2021)
- [TransFool: An Adversarial Attack against Neural Machine Translation Models](https://mlsanity.com/paper/2302.00944/) (2023)
- [Targeted Adversarial Attacks against Neural Machine Translation](https://mlsanity.com/paper/2303.01068/) (2023)
- [A neural anisotropic view of underspecification in deep learning](https://mlsanity.com/paper/2104.14372/) (2021)
- [Improving filling level classification with adversarial training](https://mlsanity.com/paper/2102.04057/) (2021)
- [Catastrophic overfitting can be induced with discriminative non-robust features](https://mlsanity.com/paper/2206.08242/) (2022)
- [On the Interaction of Compressibility and Adversarial Robustness](https://mlsanity.com/paper/2507.17725/) (2025)
- [Bio-inspired Robustness: A Review](https://mlsanity.com/paper/2103.09265/) (2021)
- [Simple black-box universal adversarial attacks on medical image classification based on deep neural networks](https://mlsanity.com/paper/2108.04979/) (2021)
- [Robustness and invariance properties of image classifiers](https://mlsanity.com/paper/2209.02408/) (2022)
- [Adversarial training with informed data selection](https://mlsanity.com/paper/2301.04472/) (2023)
- [Frequency-Based Vulnerability Analysis of Deep Learning Models against Image Corruptions](https://mlsanity.com/paper/2306.07178/) (2023)
- [A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models](https://mlsanity.com/paper/2306.08492/) (2023)
- [Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of SAR ATR](https://mlsanity.com/paper/2401.17038/) (2024)
- [S4ST: A Strong, Self-transferable, faSt, and Simple Scale Transformation for Transferable Targeted Attack](https://mlsanity.com/paper/2410.13891/) (2024)
- [Fortifying Time Series: DTW-Certified Robust Anomaly Detection](https://mlsanity.com/paper/2605.07690/) (2026)
- [Adversarial Detection by Approximation of Ensemble Boundary](https://mlsanity.com/paper/2211.10227/) (2022)
- [A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service Inference](https://mlsanity.com/paper/2304.00083/) (2023)
- [Revisiting and Advancing Adversarial Training Through A Simple Baseline](https://mlsanity.com/paper/2306.07613/) (2023)
- [Adversarial generalization of unfolding (model-based) networks](https://mlsanity.com/paper/2509.15370/) (2025)
- [Cross-Modal Attention Acts as a Frequency Filter: Why Verbose Prompts Improve Robustness in Vision-Language Models](https://mlsanity.com/paper/2609.20139/) (2026)

## Abstract (arXiv)

Driven by massive amounts of data and important advances in computational resources, new deep learning systems have achieved outstanding results in a large spectrum of applications. Nevertheless, our current theoretical understanding on the mathematical foundations of deep learning lags far behind its empirical success. Towards solving the vulnerability of neural networks, however, the field of adversarial robustness has recently become one of the main sources of explanations of our deep models. In this article, we provide an in-depth review of the field of adversarial robustness in deep learning, and give a self-contained introduction to its main notions. But, in contrast to the mainstream pessimistic perspective of adversarial robustness, we focus on the main positive aspects that it entails. We highlight the intuitive connection between adversarial examples and the geometry of deep neural networks, and eventually explore how the geometric study of adversarial examples can serve as a powerful tool to understand deep learning. Furthermore, we demonstrate the broad applicability of adversarial robustness, providing an overview of the main emerging applications of adversarial robustness beyond security. The goal of this article is to provide readers with a set of new perspectives to understand deep learning, and to supply them with intuitive tools and insights on how to use adversarial robustness to improve it.