Implicit Neural Representation Steganography by Neuron Pruning

Abstract Recently, implicit neural representation (INR) has started to be applied in image steganography. However, the quality of stego and secret images represented by INR is generally low. In this paper, we propose an implicit neural representation steganography method by neuron pruning. Initially, we randomly deactivate a portion of neurons to train an INR function for implicitly representing the secret image. Subsequently, we prune the neurons that are deemed unimportant for representing the secret image in a unstructured manner to obtain a secret function, while marking the positions of neurons as the key. Finally, based on a partial optimization strategy, we reactivate the pruned neurons to construct a stego function for representing the cover image. The recipient only needs the shared key to recover the secret function from the stego function in order to reconstruct the secret image. Experimental results demonstrate that this method not only allows for lossless recovery of the secret image, but also performs well in terms of capacity, fidelity, and undetectability. The experiments conducted on images of different resolutions validate that our proposed method exhibits significant advantages in image quality over existing implicit representation steganography methods.