Comparing the Performance of Deep Denoising Sparse Autoencoder with Other Defense Methods Against Adversarial Attacks for Arabic letters

Abstract

The aim of this paper is to compare how effectively the Deep Denoising Sparse Autoencoder (DDSA) method performs compared to other defense strategies - like adversarial training, defensive distillation and feature squeezing - in dealing with adversarial attacks for Arabic letters. We strive to evaluate both the accuracy and robustness as well as efficiency of these methods by examining a test set from the Arabic Handwritten Characters Dataset while considering adversarial attacks. Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Carlini and Wagner (C&W) are all part of this. Our research findings demonstrate that DDSA surpasses the rest of the defense methods in terms of classification accuracy and robustness. This exceptional performance is due to the distinctive attributes of DDSA, which concentrate on acquiring distinguishing features and integrating spatial information to improve defense against adversarial perturbations. While it necessitates more computational resources, DDSA's superior performance validates the additional expenses, particularly in critical applications where misclassification may have severe implications.