论文标题
使用加密特征图的访问控制模型
Access Control with Encrypted Feature Maps for Object Detection Models
论文作者
论文摘要
在本文中，我们首次提出了一种具有秘密键的访问控制方法，以便没有秘密密钥的未经授权的用户无法从受过训练的模型的性能中受益。该方法使我们不仅可以为授权用户提供高检测性能，还可以为未经授权的用户降低性能。提出了转换图像的使用用于对图像分类模型的访问控制，但是由于性能降解，这些图像不能用于对象检测模型。因此，在本文中，选定的特征图用训练和测试模型的秘密密钥加密，而不是输入图像。在一个实验中，受保护的模型允许授权用户获得与非保护模型的性能几乎相同的性能，并且具有鲁棒性，而无需键入未经授权的访问。
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.
