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Inferring the latent variable generating a given test sample is a challengingproblem in Generative Adversarial Networks (GANs). In this paper, we proposeInvGAN - a novel framework for solving the inference problem in GANs, whichinvolves training an encoder network capable of inverting a pre-trainedgenerator network without access to any training data. Under mild assumptions,we theoretically show that using InvGAN, we can approximately invert thegenerations of any latent code of a trained GAN model. Furthermore, weempirically demonstrate the superiority of our inference scheme by quantitativeand qualitative comparisons with other methods that perform a similar task. Wealso show the effectiveness of our framework in the problem of adversarialdefenses where InvGAN can successfully be used as a projection-based defensemechanism. Additionally, we show how InvGAN can be used to implementreparameterization white-box attacks on projection-based defense mechanisms.Experimental validation on several benchmark datasets demonstrate the efficacyof our method in achieving improved performance on several white-box andblack-box attacks. Our code is available athttps://github.com/yogeshbalaji/InvGAN.

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