Secure multiple image steganography based on invertible neural network and neural style transfer

Multiple image steganography involves hiding multiple secret images in a single cover image and eventually recovering all secret images from the stego image. Although the current image steganography has been greatly improved in terms of steganography capacity and invisibility, its security, as well as its steganography capacity and invisibility in style transfer scenarios are facing serious challenges. To solve these problems, an improved high-security multiple image steganography framework SMIS is proposed by combining invertible neural network and style transfer network. First, the introduction of a pre-trained style transfer network can disrupt the hidden features of the secret image embedded in the stego image, thus significantly reducing the detection accuracy of steganography. Secondly, to enable the dense invertible neural network (DINN) to extract secret images effectively, on the one hand, a feature reconstruction module (FRM) is designed based on the UNet structure to reconstruct the semantic features of the secret information, and on the other hand, a semantic reconstruction loss is designed to constrain the overall training of the steganography model. In addition, to solve the problem of limited global receptive field and excessive feature redundancy of UNet, the explicit visual center (EVC) and a self-calibration Neck (ScNeck) are introduced. The experimental results show that the proposed SMIS can efficiently hide the multiple secret images greatly improve the invisibility of steganography, and extract them from stylized stego image. At the same time, the security of the proposed method is higher than that of the existing mainstream methods.