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

arXiv: https://arxiv.org/abs/2507.12730 · HTML: https://mlsanity.com/paper/2507.12730/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Block-wise Image Transformation with Secret Key for Adversarially Robust Defense](https://mlsanity.com/paper/2010.00801/) (2020)
- [An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applications](https://mlsanity.com/paper/2201.11006/) (2022)
- [Privacy-Preserving Image Classification Using Isotropic Network](https://mlsanity.com/paper/2204.07707/) (2022)
- [A Protection Method of Trained CNN Model with Secret Key from Unauthorized Access](https://mlsanity.com/paper/2105.14756/) (2021)
- [Image and Model Transformation with Secret Key for Vision Transformer](https://mlsanity.com/paper/2207.05366/) (2022)
- [Grayscale-based Block Scrambling Image Encryption for Social Networking Services](https://mlsanity.com/paper/1806.03787/) (2018)
- [Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer](https://mlsanity.com/paper/2401.05126/) (2024)
- [A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defense](https://mlsanity.com/paper/2402.07183/) (2024)

## Built on by (most cited first, 1 of 1)

- [Privacy-Preserving Semantic Segmentation without Key Management](https://mlsanity.com/paper/2604.16523/) (2026)

## Abstract (arXiv)

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.