  Nowadays the deep learning technology is growing faster and shows dramatic
performance in computer vision areas. However, it turns out a deep learning
based model is highly vulnerable to some small perturbation called an
adversarial attack. It can easily fool the deep learning model by adding small
perturbations. On the other hand, tensor decomposition method widely uses for
compressing the tensor data, including data matrix, image, etc. In this paper,
we suggest combining tensor decomposition for defending the model against
adversarial example. We verify this idea is simple and effective to resist
adversarial attack. In addition, this method rarely degrades the original
performance of clean data. We experiment on MNIST, CIFAR10 and ImageNet data
and show our method robust on state-of-the-art attack methods.
