# Toward Robust Image Classification Source: [Toward Robust Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:50:28.993Z Authors: Basemah Alshemali, Alta Graham, Jugal Kalita Published: 2019-09-19T21:08:13.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()