# A Deep Learning-based Audio-in-Image Watermarking Scheme

arXiv: https://arxiv.org/abs/2110.02436 · HTML: https://mlsanity.com/paper/2110.02436/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [A Robust Blind Watermarking Using Convolutional Neural Network](https://mlsanity.com/paper/1704.03248/) (2017)
- [A Robust Image Watermarking System Based on Deep Neural Networks](https://mlsanity.com/paper/1908.11331/) (2019)
- [DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding](https://mlsanity.com/paper/2010.10334/) (2020)
- [Convolutional Neural Network Steganalysis's Application to Steganography](https://mlsanity.com/paper/1711.02581/) (2017)
- [Emerging Applications of Reversible Data Hiding](https://mlsanity.com/paper/1811.02928/) (2018)

## Built on by (most cited first, 3 of 3)

- [A Brief Yet In-Depth Survey of Deep Learning-Based Image Watermarking](https://mlsanity.com/paper/2308.04603/) (2023)
- [Deep Learning-based Text-in-Image Watermarking](https://mlsanity.com/paper/2404.13134/) (2024)
- [Robust Image Watermarking based on Cross-Attention and Invariant Domain Learning](https://mlsanity.com/paper/2310.05395/) (2023)

## Abstract (arXiv)

This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks can open up possibilities for different downstream applications. For the purpose of implementing an audio-in-image watermarking that adapts to the demands of increasingly diverse situations, a neural network architecture is designed to automatically learn the watermarking process in an unsupervised manner. In addition, a similarity network is developed to recognize the audio watermarks under distortions, therefore providing robustness to the proposed method. Experimental results have shown high fidelity and robustness of the proposed blind audio-in-image watermarking scheme.