# Towards Deep Learning Models Resistant to Adversarial Attacks

**Authors:** Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu  
**arXiv:** [1706.06083](https://arxiv.org/abs/1706.06083)  
**Date:** 2017-06-19  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1706.06083v4/

## Key points

- Deep neural networks are vulnerable to adversarial examples, which are inputs that resemble natural data but are misclassified by the network.
- Adversarial attacks may be an inherent weakness of deep learning models.
- The authors investigate the robustness of neural networks using a robust optimization framework.
- The approach provides a comprehensive understanding of previous work on adversarial attacks and defense mechanisms.
- The authors propose a concrete security guarantee capable of protecting against any adversary.
- The methods significantly enhance the resistance of neural networks to various types of adversarial attacks.
- The concept of security against first-order adversaries is introduced as a broad and natural security guarantee.
- Achieving robustness against well-defined classes of adversaries is seen as an essential step towards developing fully resistant deep learning models.
- Code and pre-trained models for further exploration and implementation are available on GitHub at https://github.com/MadryLab/mnist_challenge and https://github.com/MadryLab/cifar10_challenge.

## Summary

Recent research has uncovered a concerning vulnerability in deep neural networks: their susceptibility to adversarial examples. These are inputs that closely resemble natural data but are misclassified by the network. This suggests that adversarial attacks may be an inherent weakness of deep learning models. To address this issue, the authors of this study investigate the robustness of neural networks using a robust optimization framework. This approach provides a comprehensive understanding of previous work on adversarial attacks and defense mechanisms, allowing for the development of reliable methods for training and attacking neural networks with a certain degree of universality. In fact, they propose a concrete security guarantee capable of protecting against any adversary. The methods devised by the authors significantly enhance the resistance of neural networks to various types of adversarial attacks. Additionally, they introduce the concept of security against first-order adversaries as a broad and natural security guarantee. The researchers believe that achieving robustness against well-defined classes of adversaries is an essential step towards developing fully resistant deep learning models. To further explore and implement these techniques, code and pre-trained models are made available on GitHub at https://github.com/MadryLab/mnist_challenge and https://github.com/MadryLab/cifar10_challenge. In summary, this study addresses the vulnerability of deep neural networks to adversarial examples through robust optimization techniques. The proposed methods offer improved resistance against diverse adversarial attacks and lay the foundation for developing more resilient deep learning models in the future.