当前位置: X-MOL 学术Secur. Commun. Netw. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Improved CNN-Based Hashing for Encrypted Image Retrieval
Security and Communication Networks ( IF 1.968 ) Pub Date : 2021-02-26 , DOI: 10.1155/2021/5556634
Wenyan Pan 1 , Meimin Wang 1 , Jiaohua Qin 2 , Zhili Zhou 1
Affiliation  

As more and more image data are stored in the encrypted form in the cloud computing environment, it has become an urgent problem that how to efficiently retrieve images on the encryption domain. Recently, Convolutional Neural Network (CNN) features have achieved promising performance in the field of image retrieval, but the high dimension of CNN features will cause low retrieval efficiency. Also, it is not suitable to directly apply them for image retrieval on the encryption domain. To solve the above issues, this paper proposes an improved CNN-based hashing method for encrypted image retrieval. First, the image size is increased and inputted into the CNN to improve the representation ability. Then, a lightweight module is introduced to replace a part of modules in the CNN to reduce the parameters and computational cost. Finally, a hash layer is added to generate a compact binary hash code. In the retrieval process, the hash code is used for encrypted image retrieval, which greatly improves the retrieval efficiency. The experimental results show that the scheme allows an effective and efficient retrieval of encrypted images.

中文翻译:

改进的基于CNN的哈希用于加密图像检索

随着越来越多的图像数据以加密形式存储在云计算环境中,如何有效地在加密域上检索图像已经成为迫在眉睫的问题。近年来,卷积神经网络(CNN)功能在图像检索领域已取得了令人鼓舞的性能,但CNN特征的高维尺寸将导致检索效率低下。同样,将它们直接应用于加密域上的图像检索也是不合适的。为解决上述问题,本文提出了一种改进的基于CNN的哈希算法用于加密图像检索。首先,增加图像尺寸并将其输入到CNN中以提高表示能力。然后,引入了轻量级模块来替换CNN中的部分模块,以减少参数和计算成本。最后,添加哈希层以生成紧凑的二进制哈希码。在检索过程中,使用哈希码进行加密图像检索,大大提高了检索效率。实验结果表明,该方案可以有效,高效地检索加密图像。
更新日期:2021-02-26
down
wechat
bug