# Multi-Stage Residual Hiding for Image-into-Audio Steganography

arXiv: https://arxiv.org/abs/2101.01872 · HTML: https://mlsanity.com/paper/2101.01872/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Generating Steganographic Images via Adversarial Training](https://mlsanity.com/paper/1703.00371/) (2017)
- [Deep Networks for Compressed Image Sensing](https://mlsanity.com/paper/1707.07119/) (2017)
- [Steganographic Generative Adversarial Networks](https://mlsanity.com/paper/1703.05502/) (2017)
- [Invisible Steganography via Generative Adversarial Networks](https://mlsanity.com/paper/1807.08571/) (2018)
- [SSGAN: Secure Steganography Based on Generative Adversarial Networks](https://mlsanity.com/paper/1707.01613/) (2017)
- [End-to-end Trained CNN Encode-Decoder Networks for Image Steganography](https://mlsanity.com/paper/1711.07201/) (2017)
- [Heard More Than Heard: An Audio Steganography Method Based on GAN](https://mlsanity.com/paper/1907.04986/) (2019)

## Built on by (most cited first, 6 of 6)

- [StegaNeRF: Embedding Invisible Information within Neural Radiance Fields](https://mlsanity.com/paper/2212.01602/) (2022)
- [A Brief Survey on Deep Learning Based Data Hiding](https://mlsanity.com/paper/2103.01607/) (2021)
- [THInImg: Cross-modal Steganography for Presenting Talking Heads in Images](https://mlsanity.com/paper/2311.17177/) (2023)
- [WavInWav: Time-domain Speech Hiding via Invertible Neural Network](https://mlsanity.com/paper/2510.02915/) (2025)
- [Evo* 2023 -- Late-Breaking Abstracts Volume](https://mlsanity.com/paper/2403.13950/) (2024)
- [A Training-Free Proactive Defense Against Partial Speech Manipulation via Self-Embedding Steganography](https://mlsanity.com/paper/2608.25285/) (2026)

## Abstract (arXiv)

The widespread application of audio communication technologies has speeded up audio data flowing across the Internet, which made it a popular carrier for covert communication. In this paper, we present a cross-modal steganography method for hiding image content into audio carriers while preserving the perceptual fidelity of the cover audio. In our framework, two multi-stage networks are designed: the first network encodes the decreasing multilevel residual errors inside different audio subsequences with the corresponding stage sub-networks, while the second network decodes the residual errors from the modified carrier with the corresponding stage sub-networks to produce the final revealed results. The multi-stage design of proposed framework not only make the controlling of payload capacity more flexible, but also make hiding easier because of the gradual sparse characteristic of residual errors. Qualitative experiments suggest that modifications to the carrier are unnoticeable by human listeners and that the decoded images are highly intelligible.