Image steganography based on generative implicit neural representation

In deep steganography, the model size is usually related to the grid resolution of the underlying layer, and a separate neural network needs to be trained as a message extractor. We propose image steganography based on generative implicit neural representation, which breaks through the limitation of image resolution using a continuous function to represent image data and allows various kinds of multimedia data to be used as the cover image for steganography, which theoretically extends the class of carriers. Fixing a neural network as a message extractor, and transferring the training of the network to the training of the image itself, reduces the training cost and avoids the problem of exposing the steganographic behavior caused by the transmission of the message extractor. The experiment proves that the scheme is efficient, and it only takes 3 s to complete the optimization for an image with a resolution of 64 x 64 and a hiding capacity of 1 bpp, and the accuracy of message extraction reaches 100%. (c) 2024 SPIE and IS&T