论文标题
隐私保护和基于端到端的加密图像检索方案
A Privacy-Preserving and End-to-End-Based Encrypted Image Retrieval Scheme
论文作者
论文摘要
将加密技术应用于图像检索可以确保个人图像的安全性和隐私。该领域的相关研究集中在加密算法和人工特征提取的有机组合上。许多现有的加密图像检索方案无法防止功能泄漏和文件大小增加或无法实现满足的检索性能。在本文中，提出了一种新的端到端加密图像检索方案。首先，在JPEG压缩过程中使用块旋转，新的正交变换和块置换来加密图像。其次，我们结合了三胞胎损失和跨熵损失，以训练包含GMLP模块的网络模型，该模型通过端到端的学习来提取密码图像的功能。与手动特征提取（例如提取颜色直方图）相比，端到端机制可以节省人力。实验结果表明，我们的方案具有良好的检索性能，而可以确保压缩友好且无特征泄漏。
Applying encryption technology to image retrieval can ensure the security and privacy of personal images. The related researches in this field have focused on the organic combination of encryption algorithm and artificial feature extraction. Many existing encrypted image retrieval schemes cannot prevent feature leakage and file size increase or cannot achieve satisfied retrieval performance. In this paper, A new end-to-end encrypted image retrieval scheme is presented. First, images are encrypted by using block rotation, new orthogonal transforms and block permutation during the JPEG compression process. Second, we combine the triplet loss and the cross entropy loss to train a network model, which contains gMLP modules, by end-to-end learning for extracting cipher-images' features. Compared with manual features extraction such as extracting color histogram, the end-to-end mechanism can economize on manpower. Experimental results show that our scheme has good retrieval performance, while can ensure compression friendly and no feature leakage.
