  In the last few years, steganography has attracted increasing attention from
a large number of researchers since its applications are expanding further than
just the field of information security. The most traditional method is based on
digital signal processing, such as least significant bit encoding. Recently,
there have been some new approaches employing deep learning to address the
problem of steganography. However, most of the existing approaches are designed
for image-in-image steganography. In this paper, the use of deep learning
techniques to hide secret audio into the digital images is proposed. We employ
a joint deep neural network architecture consisting of two sub-models: the
first network hides the secret audio into an image, and the second one is
responsible for decoding the image to obtain the original audio. Extensive
experiments are conducted with a set of 24K images and the VIVOS Corpus audio
dataset. Through experimental results, it can be seen that our method is more
effective than traditional approaches. The integrity of both image and audio is
well preserved, while the maximum length of the hidden audio is significantly
improved.
