# Learning with a Strong Adversary

arXiv: https://arxiv.org/abs/1511.03034 · HTML: https://mlsanity.com/paper/1511.03034/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2015.

## Built on by (most cited first, 30 of 68)

- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Partial success in closing the gap between human and machine vision](https://mlsanity.com/paper/2106.07411/) (2021)
- [How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses](https://mlsanity.com/paper/2305.10862/) (2023)
- [Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems](https://mlsanity.com/paper/2401.05778/) (2024)
- [Relating Adversarially Robust Generalization to Flat Minima](https://mlsanity.com/paper/2104.04448/) (2021)
- [Constrained Learning with Non-Convex Losses](https://mlsanity.com/paper/2103.05134/) (2021)
- [Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons](https://mlsanity.com/paper/2102.05363/) (2021)
- [A Review of Formal Methods applied to Machine Learning](https://mlsanity.com/paper/2104.02466/) (2021)
- [Defending Against Image Corruptions Through Adversarial Augmentations](https://mlsanity.com/paper/2104.01086/) (2021)
- [The Role of Explainability in Assuring Safety of Machine Learning in Healthcare](https://mlsanity.com/paper/2109.00520/) (2021)
- [Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning](https://mlsanity.com/paper/2112.08691/) (2021)
- [Adversarial Attacks on Deep Models for Financial Transaction Records](https://mlsanity.com/paper/2106.08361/) (2021)
- [Fairify: Fairness Verification of Neural Networks](https://mlsanity.com/paper/2212.06140/) (2022)
- [Pixle: a fast and effective black-box attack based on rearranging pixels](https://mlsanity.com/paper/2202.02236/) (2022)
- [Regularization properties of adversarially-trained linear regression](https://mlsanity.com/paper/2310.10807/) (2023)
- [Efficient Global Optimization of Two-Layer ReLU Networks: Quadratic-Time Algorithms and Adversarial Training](https://mlsanity.com/paper/2201.01965/) (2022)
- [Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting](https://mlsanity.com/paper/2110.03135/) (2021)
- [A Person Re-identification Data Augmentation Method with Adversarial Defense Effect](https://mlsanity.com/paper/2101.08783/) (2021)
- [Adversarial Training Should Be Cast as a Non-Zero-Sum Game](https://mlsanity.com/paper/2306.11035/) (2023)
- [Analysis and Extensions of Adversarial Training for Video Classification](https://mlsanity.com/paper/2206.07953/) (2022)
- [Morphence: Moving Target Defense Against Adversarial Examples](https://mlsanity.com/paper/2108.13952/) (2021)
- [Perceptually Constrained Adversarial Attacks](https://mlsanity.com/paper/2102.07140/) (2021)
- [Adversarial Learning with Cost-Sensitive Classes](https://mlsanity.com/paper/2101.12372/) (2021)
- [Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice](https://mlsanity.com/paper/2102.01979/) (2021)
- [Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators](https://mlsanity.com/paper/2104.08323/) (2021)
- [Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting](https://mlsanity.com/paper/2104.10868/) (2021)
- [Optimization and Optimizers for Adversarial Robustness](https://mlsanity.com/paper/2303.13401/) (2023)
- [Adversarial Training for Defense Against Label Poisoning Attacks](https://mlsanity.com/paper/2502.17121/) (2025)
- [Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts](https://mlsanity.com/paper/2303.05155/) (2023)
- [Towards Speeding up Adversarial Training in Latent Spaces](https://mlsanity.com/paper/2102.00662/) (2021)

## Abstract (arXiv)

The robustness of neural networks to intended perturbations has recently attracted significant attention. In this paper, we propose a new method, learning with a strong adversary, that learns robust classifiers from supervised data. The proposed method takes finding adversarial examples as an intermediate step. A new and simple way of finding adversarial examples is presented and experimentally shown to be efficient. Experimental results demonstrate that resulting learning method greatly improves the robustness of the classification models produced.