# A Direct Approach to Robust Deep Learning Using Adversarial Networks

arXiv: https://arxiv.org/abs/1905.09591 · HTML: https://mlsanity.com/paper/1905.09591/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

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

- [Better Diffusion Models Further Improve Adversarial Training](https://mlsanity.com/paper/2302.04638/) (2023)
- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [UP5: Unbiased Foundation Model for Fairness-aware Recommendation](https://mlsanity.com/paper/2305.12090/) (2023)
- [Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart](https://mlsanity.com/paper/2105.14785/) (2021)
- [Learning Diverse-Structured Networks for Adversarial Robustness](https://mlsanity.com/paper/2102.01886/) (2021)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [Machine Learning Robustness: A Primer](https://mlsanity.com/paper/2404.00897/) (2024)
- [Enhance Diffusion to Improve Robust Generalization](https://mlsanity.com/paper/2306.02618/) (2023)
- [DeepMoM: Robust Deep Learning With Median-of-Means](https://mlsanity.com/paper/2105.14035/) (2021)
- [Robustness-via-Synthesis: Robust Training with Generative Adversarial Perturbations](https://mlsanity.com/paper/2108.09713/) (2021)
- [Clarifying Myths About the Relationship Between Shape Bias, Accuracy, and Robustness](https://mlsanity.com/paper/2406.05006/) (2024)
- [A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attacks](https://mlsanity.com/paper/2103.02654/) (2021)
- [AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks](https://mlsanity.com/paper/2203.09792/) (2022)
- [Distribution Estimation of Contaminated Data via DNN-based MoM-GANs](https://mlsanity.com/paper/2212.13741/) (2022)
- [Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack](https://mlsanity.com/paper/2304.03955/) (2023)
- [DTA: Distribution Transform-based Attack for Query-Limited Scenario](https://mlsanity.com/paper/2312.07245/) (2023)
- [Fine-Tuning Cycle-GAN for Domain Adaptation of MRI Images](https://mlsanity.com/paper/2601.12512/) (2026)
- [Generative AI and Federated Learning for Intrusion Detection Systems: A Survey](https://mlsanity.com/paper/2607.01305/) (2026)

## Abstract (arXiv)

Deep neural networks have been shown to perform well in many classical machine learning problems, especially in image classification tasks. However, researchers have found that neural networks can be easily fooled, and they are surprisingly sensitive to small perturbations imperceptible to humans. Carefully crafted input images (adversarial examples) can force a well-trained neural network to provide arbitrary outputs. Including adversarial examples during training is a popular defense mechanism against adversarial attacks. In this paper we propose a new defensive mechanism under the generative adversarial network (GAN) framework. We model the adversarial noise using a generative network, trained jointly with a classification discriminative network as a minimax game. We show empirically that our adversarial network approach works well against black box attacks, with performance on par with state-of-art methods such as ensemble adversarial training and adversarial training with projected gradient descent.