The interest of the machine learning community in image synthesis has grown significantly in recent years, with the introduction of a wide range of deep generative models and means for training them. In this work, we propose a general model-agnostic technique for improving the image quality and the distribution fidelity of generated images, obtained by any generative model. Our method, termed BIGRoC (Boosting Image Generation via a Robust Classifier), is based on a post-processing procedure via the guidance of a given robust classifier and without a need for additional training of the generative model. Given a synthesized image, we propose to update it through projected gradient steps over the robust classifier, in an attempt to refine its recognition. We demonstrate this post-processing algorithm on various image synthesis methods and show a significant improvement, both quantitatively and qualitatively, on CIFAR-10 and ImageNet. Specifically, BIGRoC improves the best performing diffusion model on ImageNet $128\times128$ by 14.81%, attaining an FID score of 2.53, and on $256\times256$ by 7.87%, achieving an FID of 3.63.