  Because of the increasing accuracy of Deep Neural Networks (DNNs) on
different tasks, a lot of real times systems are utilizing DNNs. These DNNs are
vulnerable to adversarial perturbations and corruptions. Specifically, natural
corruptions like fog, blur, contrast etc can affect the prediction of DNN in an
autonomous vehicle. In real time, these corruptions are needed to be detected
and also the corrupted inputs are needed to be de-noised to be predicted
correctly. In this work, we propose CorrGAN approach, which can generate benign
input when a corrupted input is provided. In this framework, we train
Generative Adversarial Network (GAN) with novel intermediate output-based loss
function. The GAN can denoise the corrupted input and generate benign input.
Through experimentation, we show that up to 75.2% of the corrupted
misclassified inputs can be classified correctly by DNN using CorrGAN.
