  In this research, we focus on the usage of adversarial sampling to test for
the fairness in the prediction of deep neural network model across different
classes of image in a given dataset. While several framework had been proposed
to ensure robustness of machine learning model against adversarial attack, some
of which includes adversarial training algorithm. There is still the pitfall
that adversarial training algorithm tends to cause disparity in accuracy and
robustness among different group. Our research is aimed at using adversarial
sampling to test for fairness in the prediction of deep neural network model
across different classes or categories of image in a given dataset. We
successfully demonstrated a new method of ensuring fairness across various
group of input in deep neural network classifier. We trained our neural network
model on the original image, and without training our model on the perturbed or
attacked image. When we feed the adversarial samplings to our model, it was
able to predict the original category/ class of the image the adversarial
sample belongs to. We also introduced and used the separation of concern
concept from software engineering whereby there is an additional standalone
filter layer that filters perturbed image by heavily removing the noise or
attack before automatically passing it to the network for classification, we
were able to have accuracy of 93.3%. Cifar-10 dataset have ten categories of
dataset, and so, in order to account for fairness, we applied our hypothesis
across each categories of dataset and were able to get a consistent result and
accuracy.
