# Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference

arXiv: https://arxiv.org/abs/1911.10291 · HTML: https://mlsanity.com/paper/1911.10291/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2019.

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

- [SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)](https://mlsanity.com/paper/2108.04974/) (2021)
- [Deep Intellectual Property Protection: A Survey](https://mlsanity.com/paper/2304.14613/) (2023)
- [Towards Robust Model Watermark via Reducing Parametric Vulnerability](https://mlsanity.com/paper/2309.04777/) (2023)
- [Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training](https://mlsanity.com/paper/2106.05453/) (2021)

## Abstract (arXiv)

Inferring the latent variable generating a given test sample is a challenging problem in Generative Adversarial Networks (GANs). In this paper, we propose InvGAN - a novel framework for solving the inference problem in GANs, which involves training an encoder network capable of inverting a pre-trained generator network without access to any training data. Under mild assumptions, we theoretically show that using InvGAN, we can approximately invert the generations of any latent code of a trained GAN model. Furthermore, we empirically demonstrate the superiority of our inference scheme by quantitative and qualitative comparisons with other methods that perform a similar task. We also show the effectiveness of our framework in the problem of adversarial defenses where InvGAN can successfully be used as a projection-based defense mechanism. Additionally, we show how InvGAN can be used to implement reparameterization white-box attacks on projection-based defense mechanisms. Experimental validation on several benchmark datasets demonstrate the efficacy of our method in achieving improved performance on several white-box and black-box attacks. Our code is available at https://github.com/yogeshbalaji/InvGAN.