Secure Verification Encrypted Image Retrieval Scheme with Addition Homomorphic Bitmap Index

With growing numbers of users outsourcing images to cloud servers, privacy-preserving content-based image retrieval (CBIR) is widely studied. However, existing privacy-preserving CBIR schemes have limitations in terms of low search accuracy and efficiency due to the use of unreasonable indexing structures or retrieval methods. Meanwhile, existing result verification schemes do not consider the privacy of verification information. To address these problems, we propose a new secure verification encrypted image retrieval scheme. Specifically, we design an additional homomorphic bitmap index structure by using a pre-trained CNN model with modified fully connected layers to extract image feature vectors and organize them into a bitmap. It makes the extracted features more representative and robust compared to manually designed features, and only performs vector addition during the search process, improving search efficiency and accuracy. Moreover, we design a reversible data hiding (RDH) technique with color images, which embeds the verification information into the least significant bits of the encrypted image pixels to improve the security of the verification information and reduce the storage overhead. Finally, we analyze the security of our scheme against chosen-plaintext attacks (CPA) in the security analysis and demonstrate the effectiveness of our scheme on two real-world datasets (i.e., COCO and Flickr-25k) through experiments.