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Found 6 papers, 0 papers with code
Efficient Fine-Tuning with Domain Adaptation for Privacy-Preserving Vision Transformer
We propose a novel method for privacy-preserving deep neural networks (DNNs) with the Vision Transformer (ViT).
Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images
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.
Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer
In recent years, privacy-preserving methods for deep learning have become an urgent problem.
Access Control with Encrypted Feature Maps for Object Detection Models
In an experiment, the protected models allowed authorized users to obtain almost the same performance as that of non-protected models but also with robustness against unauthorized access without a key.
An Access Control Method with Secret Key for Semantic Segmentation Models
A novel method for access control with a secret key is proposed to protect models from unauthorized access in this paper.
Access Control of Object Detection Models Using Encrypted Feature Maps
In this paper, the use of encrypted feature maps is shown to be effective in access control of object detection models for the first time.
