In recent years, secure image archival and retrieval model in cloud computing (CC) received significant attention to ensure data confidentiality and secured data transmission among cloud server and clients cloud storage and users. Traditional secure image retrieval (IR) techniques are not applicable to adapt with a large-scale IR in cloud environment. In order to overcome these issues, this article introduces an effective secure IR scheme using Inception with ResNet v2 (SIRS-IR) and Multiple Share Creation (MSC) scheme. The proposed method uses Inception with ResNet v2 model based feature extraction process. At the same time, the MSC process is utilized for multiple share generation and then encryption of shares takes place using double chaotic logistic map (DLCM) technique. Besides, the encrypted shares and the respective feature vectors are saved in the cloud server with the corresponding image identification number. During the IR process for the applied QI, the SIRS-IR model extracts the feature vectors and performs similarity measurement to retrieve the related images from the database interms of encrypted shares. Finally, the share decryption process is carried out for the reconstruction of original images with no loss of quality. Extensive experimentations were performed to verify the retrieval performance and image quality of the reconstructed images using Corel10K dataset. The obtained results stated that the presented SIRS-IR model is found to be superior to other methods in a considerable way.

近年来，云计算（CC）中的安全图像存档和检索模型受到了广泛关注，以确保数据机密性以及云服务器与客户端，云存储和用户之间的安全数据传输。传统的安全图像检索（IR）技术不适用于在云环境中适应大规模IR。为了克服这些问题，本文介绍了一种有效的安全IR方案，该方案使用带有ResNet v2的Inception（SIRS-IR）和多共享创建（MSC）方案。所提出的方法使用基于ResNet v2模型的Inception基于特征提取过程。同时，将MSC过程用于多份共享生成，然后使用双混沌逻辑映射（DLCM）技术对共享进行加密。除了，加密的份额和相应的特征向量与相应的图像标识号一起保存在云服务器中。在对所应用的QI进行IR处理期间，SIRS-IR模型提取特征向量并执行相似度测量以从数据库中以加密份额的方式检索相关图像。最后，执行共享解密过程以重建原始图像，而不会降低质量。使用Corel10K数据集进行了大量实验以验证重建图像的检索性能和图像质量。获得的结果表明，发现的SIRS-IR模型在相当大的程度上优于其他方法。SIRS-IR模型提取特征向量并执行相似度测量，以从加密份额的数据库中检索相关图像。最后，执行共享解密过程以重建原始图像，而不会降低质量。使用Corel10K数据集进行了大量实验以验证重建图像的检索性能和图像质量。获得的结果表明，发现的SIRS-IR模型在相当大的程度上优于其他方法。SIRS-IR模型提取特征向量并执行相似度测量，以从加密份额的数据库中检索相关图像。最后，执行共享解密过程以重建原始图像，而不会降低质量。使用Corel10K数据集进行了大量实验以验证重建图像的检索性能和图像质量。获得的结果表明，发现的SIRS-IR模型在相当大的程度上优于其他方法。使用Corel10K数据集进行了大量实验以验证重建图像的检索性能和图像质量。获得的结果表明，发现的SIRS-IR模型在相当大的程度上优于其他方法。使用Corel10K数据集进行了大量实验以验证重建图像的检索性能和图像质量。获得的结果表明，发现的SIRS-IR模型在相当大的程度上优于其他方法。