Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914671790915584 |
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| author | Nagamori, Teru Shiota, Sayaka Kiya, Hitoshi |
| author_facet | Nagamori, Teru Shiota, Sayaka Kiya, Hitoshi |
| contents | We propose a novel method for privacy-preserving deep neural networks (DNNs) with the Vision Transformer (ViT). The method allows us not only to train models and test with visually protected images but to also avoid the performance degradation caused from the use of encrypted images, whereas conventional methods cannot avoid the influence of image encryption. A domain adaptation method is used to efficiently fine-tune ViT with encrypted images. In experiments, the method is demonstrated to outperform conventional methods in an image classification task on the CIFAR-10 and ImageNet datasets in terms of classification accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05126 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer Nagamori, Teru Shiota, Sayaka Kiya, Hitoshi Computer Vision and Pattern Recognition Machine Learning We propose a novel method for privacy-preserving deep neural networks (DNNs) with the Vision Transformer (ViT). The method allows us not only to train models and test with visually protected images but to also avoid the performance degradation caused from the use of encrypted images, whereas conventional methods cannot avoid the influence of image encryption. A domain adaptation method is used to efficiently fine-tune ViT with encrypted images. In experiments, the method is demonstrated to outperform conventional methods in an image classification task on the CIFAR-10 and ImageNet datasets in terms of classification accuracy. |
| title | Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2401.05126 |