# Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey

arXiv: https://arxiv.org/abs/2007.00753 · HTML: https://mlsanity.com/paper/2007.00753/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 30 of 72)

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- [A Survey on the Robustness of Computer Vision Models against Common Corruptions](https://mlsanity.com/paper/2305.06024/) (2023)
- [Zero-Query Transfer Attacks on Context-Aware Object Detectors](https://mlsanity.com/paper/2203.15230/) (2022)
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- [Resilient Constrained Learning](https://mlsanity.com/paper/2306.02426/) (2023)
- [Improving Robustness by Enhancing Weak Subnets](https://mlsanity.com/paper/2201.12765/) (2022)
- [Pruning Adversarially Robust Neural Networks without Adversarial Examples](https://mlsanity.com/paper/2210.04311/) (2022)
- [Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues](https://mlsanity.com/paper/2303.08106/) (2023)
- [Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions](https://mlsanity.com/paper/2403.20254/) (2024)
- [Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models](https://mlsanity.com/paper/2406.07145/) (2024)
- [A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm](https://mlsanity.com/paper/2310.00708/) (2023)
- [Model Preserving Compression for Neural Networks](https://mlsanity.com/paper/2108.00065/) (2021)
- [Defending Black-box Skeleton-based Human Activity Classifiers](https://mlsanity.com/paper/2203.04713/) (2022)
- [Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images](https://mlsanity.com/paper/2302.06060/) (2023)
- [Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods](https://mlsanity.com/paper/2308.06703/) (2023)
- [Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection System](https://mlsanity.com/paper/2310.03334/) (2023)
- [Machine Learning Robustness: A Primer](https://mlsanity.com/paper/2404.00897/) (2024)
- [Understanding Model Ensemble in Transferable Adversarial Attack](https://mlsanity.com/paper/2410.06851/) (2024)
- [Learning for routing: A guided review of recent developments and future directions](https://mlsanity.com/paper/2507.00218/) (2025)
- [Data Optimization in Deep Learning: A Survey](https://mlsanity.com/paper/2310.16499/) (2023)
- [Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey](https://mlsanity.com/paper/2404.05219/) (2024)
- [On the Robustness of Explanations of Deep Neural Network Models: A Survey](https://mlsanity.com/paper/2211.04780/) (2022)
- [Maintaining Adversarial Robustness in Continuous Learning](https://mlsanity.com/paper/2402.11196/) (2024)

## Abstract (arXiv)

As we seek to deploy machine learning models beyond virtual and controlled domains, it is critical to analyze not only the accuracy or the fact that it works most of the time, but if such a model is truly robust and reliable. This paper studies strategies to implement adversary robustly trained algorithms towards guaranteeing safety in machine learning algorithms. We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Certified Defenses. We survey the most recent and important results in adversarial example generation, defense mechanisms with adversarial (re)Training as their main defense against perturbations. We also survey mothods that add regularization terms that change the behavior of the gradient, making it harder for attackers to achieve their objective. Alternatively, we've surveyed methods which formally derive certificates of robustness by exactly solving the optimization problem or by approximations using upper or lower bounds. In addition, we discuss the challenges faced by most of the recent algorithms presenting future research perspectives.