Privacy-Preserving Semantic Segmentation without Key Management

Fuente: arXiv
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Main Authors: Hirose, Mare, Imaizumi, Shoko, Kiya, Hitoshi
Format: Preprint
Published: 2026
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author Hirose, Mare
Imaizumi, Shoko
Kiya, Hitoshi
author_facet Hirose, Mare
Imaizumi, Shoko
Kiya, Hitoshi
contents This paper proposes a novel privacy-preserving semantic segmentation method that can use independent keys for each client and image. In the proposed method, the model creator and each client encrypt images using locally generated keys, and model training and inference are conducted on the encrypted images. To mitigate performance degradation, an image encryption method is applied to model training in addition to the generation of test images. In experiments, the effectiveness of the proposed method is confirmed on the Cityscapes dataset under the use of a vision transformer-based model, called SETR.
format Preprint
id arxiv_https___arxiv_org_abs_2604_16523
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Privacy-Preserving Semantic Segmentation without Key Management
Hirose, Mare
Imaizumi, Shoko
Kiya, Hitoshi
Computer Vision and Pattern Recognition
Cryptography and Security
This paper proposes a novel privacy-preserving semantic segmentation method that can use independent keys for each client and image. In the proposed method, the model creator and each client encrypt images using locally generated keys, and model training and inference are conducted on the encrypted images. To mitigate performance degradation, an image encryption method is applied to model training in addition to the generation of test images. In experiments, the effectiveness of the proposed method is confirmed on the Cityscapes dataset under the use of a vision transformer-based model, called SETR.
title Privacy-Preserving Semantic Segmentation without Key Management
topic Computer Vision and Pattern Recognition
Cryptography and Security
url https://arxiv.org/abs/2604.16523