Markov Process Based Retrieval for Encrypted JPEG Images

This work presents a retrieval scheme for encrypted JPEG images based on Markov process. In our scheme, the stream cipher and permutation encryption are combined to encrypt JPEG images, which are then uploaded to a database server. After that, the server without knowing the original content can extract features from the transition probability matrices of the AC coefficients of encrypted query image, in which those coefficients are modeled by Markov process. With the multi-class support vector machine (SVM), the features of encrypted query image can be converted into a vector with low dimensionality determined by the number of image categories. The encrypted database images are conducted similarly. After low-dimensional vector representation, the similarity between encrypted query image and database image may be measured by calculating the distance of their corresponding vectors. At the client side, the encrypted images returned by the server are decrypted to the plaintext images using encryption key. The proposed scheme can preserve file compliance and file size for encrypted JPEG images, while providing privacy-preserving image retrieval.