使用带密钥的分块图像加密保护语义分割模型不被非法访问
Protecting Semantic Segmentation Models by Using Block-wise Image Encryption with Secret Key from Unauthorized Access
摘要：
由于生产级训练的深度神经网络（DNN）具有巨大的商业价值，因此保护此类DNN模型免受版权侵犯和未经授权的访问的需求越来越大。然而，传统的模型保护方法只关注图像分类任务，这些保护方法从未应用于语义分割，尽管它有越来越多的应用。在本文中，我们提出了保护语义分割模型未经授权的访问，利用块式变换与密钥的第一次。受保护的模型通过使用变换后的图像进行训练。实验结果表明，该保护方法允许具有正确密钥的合法用户访问该模型的全部容量，并降低了非授权用户的性能。然而，与非保护模型相比，受保护模型的分割性能略有下降。
英文摘要：
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. However, conventional model protection methods focused only the image classification task, and these protection methods were never applied to semantic segmentation although it has an increasing number of applications. In this paper, we propose to protect semantic segmentation models from unauthorized access by utilizing block-wise transformation with a secret key for the first time. Protected models are trained by using transformed images. Experiment results show that the proposed protection method allows rightful users with the correct key to access the model to full capacity and deteriorate the performance for unauthorized users. However, protected models slightly drop the segmentation performance compared to non-protected models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
