A Deep Learning-based Audio-in-Image Watermarking Scheme
This work addresses the need for covert audio-in-image watermarking for diverse applications, though it appears incremental in its method.
The paper tackles the problem of embedding and extracting audio watermarks in images using a deep learning approach, achieving high fidelity and robustness as demonstrated in experiments.
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.