  Deep neural networks are learning models having achieved state of the art
performance in many fields like prediction, computer vision, language
processing and so on. However, it has been shown that certain inputs exist
which would not trick a human normally, but may mislead the model completely.
These inputs are known as adversarial inputs. These inputs pose a high security
threat when such models are used in real world applications. In this work, we
have analyzed the resistance of three different classes of fully connected
dense networks against the rarely tested non-gradient based adversarial
attacks. These classes are created by manipulating the input and output layers.
We have proven empirically that owing to certain characteristics of the
network, they provide a high robustness against these attacks, and can be used
in fine tuning other models to increase defense against adversarial attacks.
