FACSNet: Forensics aided content selection network for heterogeneous image steganalysis.

The main goal of image steganalysis, as a technique of confrontation with steganography, is to determine the presence or absence of secret information in conjunction with the specific statistical characteristics of the carrier. With the development of deep learning technology in recent years, the performance of steganography has been gradually enhanced. Especially for the complex reality environment, the image content is mixed and heterogeneous, which brings great challenges to the practical application of image steganalysis technology.

A fine-grained reversible data hiding in encrypted domain based on the cipher-text redundancy of encryption process.

Considering the granularity of embedded data in the design of reversible data hiding scheme has important research significance for the permission control and management of multi-granularity information. To broaden the application possibilities of encrypted data in cloud environments, the researchers propose a fine-grained reversible data hiding method leveraging the cipher-text redundancy of ElGamal encryption. Initially, prior to the encryption process, pixels are organized into a full binary tree based on fine-grained access permissions.