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pro vyhledávání: '"Nagamori, Teru"'
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
Externí odkaz:
http://arxiv.org/abs/2401.05126
In recent years, deep neural networks (DNNs) trained with transformed data have been applied to various applications such as privacy-preserving learning, access control, and adversarial defenses. However, the use of transformed data decreases the per
Externí odkaz:
http://arxiv.org/abs/2309.02556
This article presents block-wise image encryption for the vision transformer and its applications. Perceptual image encryption for deep learning enables us not only to protect the visual information of plain images but to also embed unique features c
Externí odkaz:
http://arxiv.org/abs/2308.07612
Autor:
Nagamori, Teru, Kiya, Hitoshi
In recent years, privacy-preserving methods for deep learning have become an urgent problem. Accordingly, we propose the combined use of federated learning (FL) and encrypted images for privacy-preserving image classification under the use of the vis
Externí odkaz:
http://arxiv.org/abs/2301.09255
In this paper, we propose an access control method with a secret key for object detection models for the first time so that unauthorized users without a secret key cannot benefit from the performance of trained models. The method enables us not only
Externí odkaz:
http://arxiv.org/abs/2209.14831
A novel method for access control with a secret key is proposed to protect models from unauthorized access in this paper. We focus on semantic segmentation models with the vision transformer (ViT), called segmentation transformer (SETR). Most existin
Externí odkaz:
http://arxiv.org/abs/2208.13135
In this paper, we propose an access control method for object detection models. The use of encrypted images or encrypted feature maps has been demonstrated to be effective in access control of models from unauthorized access. However, the effectivene
Externí odkaz:
http://arxiv.org/abs/2202.00265
Akademický článek
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Publikováno v:
Journal of Imaging; Sep2022, Vol. 8 Issue 9, pN.PAG-N.PAG, 14p
