基于迁移学习的秘密密钥模型保护
Transfer Learning-Based Model Protection With Secret Key
摘要：
我们提出了一种新的方法来保护训练模型与秘密密钥，使未经授权的用户没有正确的密钥不能得到正确的推断。通过利用迁移学习，所提出的方法使我们能够训练一个大型的受保护模型，就像使用ImageNet训练的模型一样，通过使用训练数据集的一个小子集。它利用一个可学习的加密步骤与一个秘密密钥，以产生可学习的变换图像。具有预先训练的权重的模型通过使用这种变换的图像进行微调。在ImageNet数据集的实验中，当给出正确的密钥时，受保护模型的性能接近于未受保护模型的性能，而当使用不正确的密钥时，准确性大幅下降。保护模型也被证明对密钥估计攻击具有鲁棒性。
英文摘要：
We propose a novel method for protecting trained models with a secret key so that unauthorized users without the correct key cannot get the correct inference. By taking advantage of transfer learning, the proposed method enables us to train a large protected model like a model trained with ImageNet by using a small subset of a training dataset. It utilizes a learnable encryption step with a secret key to generate learnable transformed images. Models with pre-trained weights are fine-tuned by using such transformed images. In experiments with the ImageNet dataset, it is shown that the performance of a protected model was close to that of a non-protected model when the correct key was given, while the accuracy tremendously dropped when an incorrect key was used. The protected model was also demonstrated to be robust against key estimation attacks.
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。
