Lossless image steganography: Regard steganography as super-resolution

Abstract

Image steganography attempts to imperceptibly hide the secret image within the cover image. Most of the existing deep learning-based steganography approaches have excelled in payload capacity, visual quality, and steganographic security. However, they are difficult to losslessly reconstruct secret images from stego images with relatively large payload capacity. Recently, although some studies have introduced invertible neural networks (INNs) to achieve large-capacity image steganography, these methods still cannot reconstruct the secret image losslessly due to the existence of lost information on the output side of the concealing network. We present an INN-based framework in this paper for lossless image steganography. Specifically, we regard image steganography as an image super-resolution task that converts low-resolution cover images to high-resolution stego images while hiding secret images. The feature dimension of the generated stego image matches the total dimension of the input secret and cover images, thereby eliminating the lost information. Besides, a bijective secret projection module is designed to transform various secret images into a latent variable that follows a simple distribution, improving the imperceptibility of the secret image. Comprehensive experiments indicate that the proposed framework achieves secure hiding and lossless extraction of the secret image.