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Nowadays, Deep learning techniques show dramatic performance on computervision area, and they even outperform human. But it is also vulnerable to somesmall perturbation called an adversarial attack. This is a problem combinedwith the safety of artificial intelligence, which has recently been studied alot. These attacks have shown that they can fool models of imageclassification, semantic segmentation, and object detection. We point out thisattack can be protected by denoise autoencoder, which is used for denoising theperturbation and restoring the original images. We experiment with variousnoise distributions and verify the effect of denoise autoencoder againstadversarial attack in semantic segmentation.

DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation