  Neural networks are frequently used for image classification, but can be
vulnerable to misclassification caused by adversarial images. Attempts to make
neural network image classification more robust have included variations on
preprocessing (cropping, applying noise, blurring), adversarial training, and
dropout randomization. In this paper, we implemented a model for adversarial
detection based on a combination of two of these techniques: dropout
randomization with preprocessing applied to images within a given Bayesian
uncertainty. We evaluated our model on the MNIST dataset, using adversarial
images generated using Fast Gradient Sign Method (FGSM), Jacobian-based
Saliency Map Attack (JSMA) and Basic Iterative Method (BIM) attacks. Our model
achieved an average adversarial image detection accuracy of 97%, with an
average image classification accuracy, after discarding images flagged as
adversarial, of 99%. Our average detection accuracy exceeded that of recent
papers using similar techniques.
