  Deep learning models, while achieving state-of-the-art performance on many
tasks, are susceptible to adversarial attacks that exploit inherent
vulnerabilities in their architectures. Adversarial attacks manipulate the
input data with imperceptible perturbations, causing the model to misclassify
the data or produce erroneous outputs. This work is based on enhancing the
robustness of targeted classifier models against adversarial attacks. To
achieve this, an convolutional autoencoder-based approach is employed that
effectively counters adversarial perturbations introduced to the input images.
By generating images closely resembling the input images, the proposed
methodology aims to restore the model's accuracy.
