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Since production-level trained deep neural networks (DNNs) are of a greatbusiness value, protecting such DNN models against copyright infringement andunauthorized access is in a rising demand. However, conventional modelprotection methods focused only the image classification task, and theseprotection methods were never applied to semantic segmentation although it hasan increasing number of applications. In this paper, we propose to protectsemantic segmentation models from unauthorized access by utilizing block-wisetransformation with a secret key for the first time. Protected models aretrained by using transformed images. Experiment results show that the proposedprotection method allows rightful users with the correct key to access themodel to full capacity and deteriorate the performance for unauthorized users.However, protected models slightly drop the segmentation performance comparedto non-protected models.

Protecting Semantic Segmentation Models by Using Block-wise Image Encryption with Secret Key from Unauthorized Access