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.
翻译:将加密技术应用到图像检索中可以确保个人图像的安全和隐私。 该领域的相关研究侧重于加密算法和人工特征提取的有机组合。 许多现有的加密图像检索方案无法防止特征泄漏和文件大小增加,或无法实现满意的检索性能。 本文介绍了一个新的端对端加密图像检索方案。 首先, 在 JPEG 压缩过程中, 图像通过使用块旋转、 新的正统变换和块变换进行加密。 其次, 我们结合了三重损失和交叉加密损失来培训网络模型, 该模型包含 gMLP 模块, 通过从端到端学习提取密码图像特性。 与手工提取特征相比, 如提取颜色直方图, 端到端机制可以在人力上实现生态化。 实验结果显示, 我们的计划具有良好的检索性能, 同时可以确保压缩友好和无特征泄漏。