  Deep Neural Networks (DNN) have become a promising paradigm when developing
Artificial Intelligence (AI) and Machine Learning (ML) applications. However,
DNN applications are vulnerable to fake data that are crafted with adversarial
attack algorithms. Under adversarial attacks, the prediction accuracy of DNN
applications suffers, making them unreliable. In order to defend against
adversarial attacks, we introduce a novel noise-reduction procedure, Vector
Quantization U-Net (VQUNet), to reduce adversarial noise and reconstruct data
with high fidelity. VQUNet features a discrete latent representation learning
through a multi-scale hierarchical structure for both noise reduction and data
reconstruction. The empirical experiments show that the proposed VQUNet
provides better robustness to the target DNN models, and it outperforms other
state-of-the-art noise-reduction-based defense methods under various
adversarial attacks for both Fashion-MNIST and CIFAR10 datasets. When there is
no adversarial attack, the defense method has less than 1% accuracy degradation
for both datasets.
