# ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks

arXiv: https://arxiv.org/abs/1904.08516 · HTML: https://mlsanity.com/paper/1904.08516/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle](https://mlsanity.com/paper/2206.12292/) (2022)
- [A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples](https://mlsanity.com/paper/2109.01275/) (2021)
- [Attacks and Defenses for Free-Riders in Multi-Discriminator GAN](https://mlsanity.com/paper/2201.09967/) (2022)

## Abstract (arXiv)

Neural Network classifiers have been used successfully in a wide range of applications. However, their underlying assumption of attack free environment has been defied by adversarial examples. Researchers tried to develop defenses; however, existing approaches are still far from providing effective solutions to this evolving problem. In this paper, we design a generative adversarial net (GAN) based zero knowledge adversarial training defense, dubbed ZK-GanDef, which does not consume adversarial examples during training. Therefore, ZK-GanDef is not only efficient in training but also adaptive to new adversarial examples. This advantage comes at the cost of small degradation in test accuracy compared to full knowledge approaches. Our experiments show that ZK-GanDef enhances test accuracy on adversarial examples by up-to 49.17% compared to zero knowledge approaches. More importantly, its test accuracy is close to that of the state-of-the-art full knowledge approaches (maximum degradation of 8.46%), while taking much less training time.