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

Source: [A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique](<https://www.sota2.com/research/paper/68fa7a0a1e209131cea80bf9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:19.152Z

Authors: Homare Sueyoshi, Kiyoshi Nishikawa, Hitoshi Kiya

Published: 2025-07-17T02:14:50.000Z

## Abstract

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.

## 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](<https://arxiv.org/abs/2507.12730>)

[PDF](<https://arxiv.org/pdf/2507.12730.pdf>)