Access Control with Encrypted Feature Maps for Object Detection Models
This addresses the need for model protection in object detection, an incremental advance over prior work on image classification.
The paper tackles the problem of unauthorized use of object detection models by proposing an access control method that encrypts selected feature maps with a secret key, allowing authorized users to achieve nearly the same performance as non-protected models while degrading performance for unauthorized users.
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 to provide a high detection performance to authorized users but to also degrade the performance for unauthorized users. The use of transformed images was proposed for the access control of image classification models, but these images cannot be used for object detection models due to performance degradation. Accordingly, in this paper, selected feature maps are encrypted with a secret key for training and testing models, instead of input images. 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.