What is the input to the generator in Generative Adversarial Network
The input to the generator is a z-dimensional vector of completely random values. In case of DCGAN, the input is from gaussian distribution source. GANs' theory is based on the fact that these random values are learnt to be distotred by the network in such a way that Discriminator/ Critic is fooled by the image that is produced by the generator. Both generator and discriminator are adversaries of each other making each other better epoch by epoch, and hence the name Adversarial networks.
For classifying an image into cars and not car, you can actually solve this problem fairly easily and accurately using a simple ConvNet by training it on multiple car images and "not car" images. There are already pretty complex object detection networks out there trained on Imagenet data, so you might as well start by referring the architecture of those networks.
Hope my answer was of some help to you. :)
I am recently studying about GAN model and thought that it would be useful for my system in which I am going to predict whether a given image is car or not. I understand the part that the "discriminator" gets input from the "generator". The generator generates image from a random vector which is then passed to the discriminator for authenticity check. But what exactly is that vector that is used by the generator ? Is it in an image converted to pixels and we pass it as an vector of pixels ?
Can anyone please explain me this 
Or is there any other method that I should follow to build a system that can classify images to car or not
Thanks in advance