A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique
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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.
Key facts. Cited by 1 paper 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
10 of 21 references.
References · 10
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,369
- 2020Block-wise Image Transformation with Secret Key for Adversarially Robust Defensecited by 30
- 2022An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applicationscited by 29
- 2022Privacy-Preserving Image Classification Using Isotropic Networkcited by 17
- 2021A Protection Method of Trained CNN Model with Secret Key from Unauthorized Accesscited by 15
- 2022Image and Model Transformation with Secret Key for Vision Transformercited by 12
- 2018Grayscale-based Block Scrambling Image Encryption for Social Networking Servicescited by 6
- 2024Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformercited by 3
- 2024A Random Ensemble of Encrypted Vision Transformers for Adversarially Robust Defensecited by 3
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Citing papers per year: 2026: 1.
1 paper.