Say the image: Auditory masking effect-driven invertible network for progressive image-in-audio steganography

In this paper, we propose an auditory masking effect-driven invertible network for Hiding an Image within an Audio signal, termed as HIA-Net. Unlike the direct hiding manner, the proposed HIA-Net decomposes the image-in-audio steganography process into two cascaded stages. In the first stage, we develop a Masker Audio Extraction (MAE) algorithm to turn the original cover audio into a masker audio. The generated masker audio exhibits higher masking capability, thereby enhancing the hiding invisibility and security. Then, we design three Image-in-Audio Invertible (I-AI) sub-networks to embed the secret image into the masker audio, yielding a stego masker audio. In the second stage, an Audio-in-Audio Invertible (A-AI) sub-network is employed to further conceal the stego masker audio within the original cover audio, producing the final stego audio. During the revealing process, the reversible architecture of the proposed network first reconstructs the stego masker from the final stego audio, and then recovers the hidden image from the stego masker. Experimental results demonstrate that HIA-Net significantly outperforms other state-of-the-art image-in-audio steganography methods, achieving a significant PSNR improvement of more than 3.0 dB for secret image reconstruction on different image and audio datasets. The user study also confirms the superior imperceptibility of the stego audios. The software code is available at https://github.com/c4Tch3r/HIANet .