  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.
