# Generating Adversarial Examples With Conditional Generative Adversarial Net

arXiv: https://arxiv.org/abs/1903.07282 · HTML: https://mlsanity.com/paper/1903.07282/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective](https://mlsanity.com/paper/2302.09457/) (2023)
- [BOSS: Bidirectional One-Shot Synthesis of Adversarial Examples](https://mlsanity.com/paper/2108.02756/) (2021)
- [Towards Cross-Domain Multi-Targeted Adversarial Attacks](https://mlsanity.com/paper/2505.20782/) (2025)
- [Quantification of Uncertainty with Adversarial Models in Medical Image Segmentation](https://mlsanity.com/paper/2606.18860/) (2026)

## Abstract (arXiv)

Recently, deep neural networks have significant progress and successful application in various fields, but they are found vulnerable to attack instances, e.g., adversarial examples. State-of-art attack methods can generate attack images by adding small perturbation to the source image. These attack images can fool the classifier but have little impact to human. Therefore, such attack instances are difficult to generate by searching the feature space. How to design an effective and robust generating method has become a spotlight. Inspired by adversarial examples, we propose two novel generative models to produce adaptive attack instances directly, in which conditional generative adversarial network is adopted and distinctive strategy is designed for training. Compared with the common method, such as Fast Gradient Sign Method, our models can reduce the generating cost and improve robustness and has about one fifth running time for producing attack instance.