In this paper, we propose an access control method that uses the spatially invariant permutation of feature maps with a secret key for protecting semantic segmentation models. Segmentation models are trained and tested by permuting selected feature maps with a secret key. The proposed method allows rightful users with the correct key not only to access a model to full capacity but also to degrade the performance for unauthorized users. Conventional access control methods have focused only on image classification tasks, and these methods have never been applied to semantic segmentation tasks. 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. In addition, a conventional method with block-wise transformations was also verified to have degraded performance under semantic segmentation models.

翻译：在本文中,我们建议使用一种出入控制方法,使用地貌图的空间变异性变异式,并配有保护语义分解模型的秘密钥匙。分解模型通过使用秘密钥匙对选定地貌图进行训练和测试。拟议方法使拥有正确钥匙的合法用户不仅能够完全进入模型,而且可以降低未经授权用户的性能。常规出入控制方法仅侧重于图像分类任务,这些方法从未适用于语义分解任务。在一次实验中,被保护的模式被证明能够使合法用户获得与非受保护模式几乎相同的性能,但也能够有力地防止未经授权的用户在没有钥匙的情况下进入。此外,还核实了一种具有区划式变换作用的传统方法,在语义分解模型下已经退化了性能。