  Nowadays, Deep learning techniques show dramatic performance on computer
vision area, and they even outperform human. But it is also vulnerable to some
small perturbation called an adversarial attack. This is a problem combined
with the safety of artificial intelligence, which has recently been studied a
lot. These attacks have shown that they can fool models of image
classification, semantic segmentation, and object detection. We point out this
attack can be protected by denoise autoencoder, which is used for denoising the
perturbation and restoring the original images. We experiment with various
noise distributions and verify the effect of denoise autoencoder against
adversarial attack in semantic segmentation.
