Securing Privacy: Encrypted Image Retrieval with CNNs and Chaos-Based Visual Cryptography on Cloud Computing
International journal of intelligent engineering and systems · 2023
Data security and accessibility in a cloud computing environment are successfully guaranteed by the safe privacy content-based image retrieval (CBIR) approach.However, ineffective cipher text methods and featureextraction techniques might reduce performance.Therefore, in order to create secure privacy CBIR, it is still necessary to address the need for high-security encryption techniques and the capacity to extract characteristics from cipher text images.To achieve this, we provide a secure privacy image retrieval method based on convolutional neural network features.Initially, a 3D Lotka_Volterra chaotic systems encryption method based on visual cryptography encodes images, and the updated DenseNet-121 model is fine-tuned by employing the encrypted images to construct a feature extractor.There are multi-authorized users set up for the fine-tuning feature extractor.The encrypted features and images are uploaded to a cloud server.Experiments on the Corel10k dataset showed the effectiveness of the proposed model in terms of security and precision compared to previous works, achieving an average search precision of 68.94% and an MSE =12764.27263,PSNR = 7.300480125 dB between the original image and encrypted, and the decrypted image is entirely identical to the original image.