  Minute pixel changes in an image drastically change the prediction that the
deep learning model makes. One of the most significant problems that could
arise due to this, for instance, is autonomous driving. Many methods have been
proposed to combat this with varying amounts of success. We propose a 3 step
method for defending such attacks. First, we denoise the image using
statistical methods. Second, we show that adopting multiple color spaces in the
same model can help us to fight these adversarial attacks further as each color
space detects certain features explicit to itself. Finally, the feature maps
generated are enlarged and sent back as an input to obtain even smaller
features. We show that the proposed model does not need to be trained to defend
an particular type of attack and is inherently more robust to black-box,
white-box, and grey-box adversarial attack techniques. In particular, the model
is 56.12 percent more robust than compared models in case of white box attacks
when the models are not subject to adversarial example training.
