High capacity reversible steganography in encrypted images based on feature mining in plaintext domain

A reversible steganographic scheme in encrypted images with high capacity based on feature mining in plaintext domain is proposed in this paper. Two techniques are used: multi-granularity encryption and residual histogram shifting. Firstly, a cover image is encrypted both on fine-grained level and coarse-grained level with content-owner key. Then, the additional data can be embedded into the encrypted image by exploring both the similarity of neighbouring pixels in local level and residual histogram in global level with data-hiding key. For legal receivers, image decryption and data extraction can be free to choose. If content-owner key and data-hiding key are both adopted at the same time, the cover image can be restored error-free along with data extraction. Experimental results show that the proposed scheme significantly outperforms the previous approaches both in terms of embedding quality and embedding capacity.