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Found 6 papers, 0 papers with code
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.
Access Control of Semantic Segmentation Models Using Encrypted Feature Maps
In this paper, we propose an access control method with a secret key for semantic segmentation models for the first time so that unauthorized users without a secret key cannot benefit from the performance of trained models.
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.
Access Control Using Spatially Invariant Permutation of Feature Maps for Semantic Segmentation Models
In an experiment, the protected models were demonstrated to allow rightful users to obtain almost the same performance as that of non-protected models but also to be robust against access by unauthorized users without a key.
Protecting Semantic Segmentation Models by Using Block-wise Image Encryption with Secret Key from Unauthorized Access
Since production-level trained deep neural networks (DNNs) are of a great business value, protecting such DNN models against copyright infringement and unauthorized access is in a rising demand.
Image Transformation Network for Privacy-Preserving Deep Neural Networks and Its Security Evaluation
We propose a transformation network for generating visually-protected images for privacy-preserving DNNs.
