Reversible data hiding in encrypted images with multi-prediction and adaptive Huffman encoding

Abstract

Abstract With the rapid development of network technology and the massive accumulation of user data, huge amounts of data are being rapidly generated and shared on the network, while the problems of unauthorized data access and misuse continue to emerge. Reversible data hiding in encrypted images (RDHEI) is a privacy-preserving method that embeds protected data into encrypted content and accurately extracts the embedded data without affecting the original content. However, the amount of embedded protected information has always been one of the major constraints on the performance and application of RDHEI. Currently, the main approaches to improve the net embedding capacity of RDHEI are to increase the total embedding capacity or to reduce the length of the auxiliary information to be embedded. In this paper, we propose a novel RDHEI scheme based on multi-prediction and adaptive Huffman encoding. To increase the total embedding capacity, we use the MED + GAP predictor to generate the label map data of non-reference pixels before image encryption. Then, an adaptive Huffman coding is designed to compress the generated labels to reduce the embedding length of the auxiliary information used for extraction and recovery. Experimental results show that with MED + GAP predictor and adaptive Huffman coding, the proposed method achieves a higher embedding capacity than other recent methods while ensuring security and reversibility.