Invert and Defend: Model-based Approximate Inversion of Generative Adversarial Networks for Secure Inference
arXiv:1911.10291
Abstract
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.
References in corpus (10)
- Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- Improved Techniques for Training GANs
- Adversarial Feature Learning
- Adversarially Learned Inference
- Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
- Differentially Private Generative Adversarial Network
- Compressed Sensing using Generative Models
- Defending Against Adversarial Attacks by Leveraging an Entire GAN