Secure and efficient content-based image retrieval using dominant local patterns and watermark encryption in cloud computing

Secure and efficient content-based image retrieval using dominant local patterns and watermark encryption in cloud computing

연구 분야: Cryptography

학회: Cluster Computing

The relevance of images in people's daily lives is growing, and content-based image retrieval (CBIR) has received a lot of attention in research. Images are much better at communicating information than text documents. This paper deals with security and efficient retrieval of images based on the texture features extracted by the dominant local patterns of an image in cloud. Here, we proposed a method that supports secure and efficient image retrieval over cloud. The images are encrypted with the watermark before deploying the image database to the cloud, this process prevents from the outflow of sensitive information to the cloud. A reduced dimension feature vector database has been created for all the images using relative directional edge patterns (RDEP), facilitating efficient storage and retrieval. The significance of the specified local pattern for effectively extracting texture information has been demonstrated. A notable level of accuracy has been established when compared to existing algorithms in terms of precision and recall. Additionally, a watermark-based system is proposed to prevent unauthorized query users from illicitly copying and distributing the acquired images to others. An inimitable watermark is entrenched into the image by the encryption module before storing into the cloud. Hence, when an image copy is discovered, the watermark extraction can be used to track down the illegal query image user who circulated the image. The proposed method's significance is assessed by comparing it to other existing feature extractors incorporating watermark encryption. Additionally, the effectiveness of the method is demonstrated across various numbers of watermark bits. Trials and security analyses affirm that the suggested approach is both robust and efficient.