  The field of steganography has experienced a surge of interest due to the
recent advancements in AI-powered techniques, particularly in the context of
multimodal setups that enable the concealment of signals within signals of a
different nature. The primary objectives of all steganographic methods are to
achieve perceptual transparency, robustness, and large embedding capacity -
which often present conflicting goals that classical methods have struggled to
reconcile. This paper extends and enhances an existing image-in-audio deep
steganography method by focusing on improving its robustness. The proposed
enhancements include modifications to the loss function, utilization of the
Short-Time Fourier Transform (STFT), introduction of redundancy in the encoding
process for error correction, and buffering of additional information in the
pixel subconvolution operation. The results demonstrate that our approach
outperforms the existing method in terms of robustness and perceptual
transparency.
