# DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation Source: [DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:11:14.110Z Authors: Seungju Cho, Tae Joon Jun, Byungsoo Oh, Daeyoung Kim Published: 2019-08-14T16:13:00.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()