A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique

Fuente: arXiv
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Main Authors: Sueyoshi, Homare, Nishikawa, Kiyoshi, Kiya, Hitoshi
Format: Preprint
Published: 2025
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author Sueyoshi, Homare
Nishikawa, Kiyoshi
Kiya, Hitoshi
author_facet Sueyoshi, Homare
Nishikawa, Kiyoshi
Kiya, Hitoshi
contents We propose a privacy-preserving semantic-segmentation method for applying perceptual encryption to images used for model training in addition to test images. This method also provides almost the same accuracy as models without any encryption. The above performance is achieved using a domain-adaptation technique on the embedding structure of the Vision Transformer (ViT). The effectiveness of the proposed method was experimentally confirmed in terms of the accuracy of semantic segmentation when using a powerful semantic-segmentation model with ViT called Segmentation Transformer.
format Preprint
id arxiv_https___arxiv_org_abs_2507_12730
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique
Sueyoshi, Homare
Nishikawa, Kiyoshi
Kiya, Hitoshi
Computer Vision and Pattern Recognition
Cryptography and Security
We propose a privacy-preserving semantic-segmentation method for applying perceptual encryption to images used for model training in addition to test images. This method also provides almost the same accuracy as models without any encryption. The above performance is achieved using a domain-adaptation technique on the embedding structure of the Vision Transformer (ViT). The effectiveness of the proposed method was experimentally confirmed in terms of the accuracy of semantic segmentation when using a powerful semantic-segmentation model with ViT called Segmentation Transformer.
title A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique
topic Computer Vision and Pattern Recognition
Cryptography and Security
url https://arxiv.org/abs/2507.12730