  Audio steganography aims at concealing secret information in carrier audio
with imperceptible modification on the carrier. Although previous works
addressed the robustness of concealed message recovery against distortions
introduced during transmission, they do not address the robustness against
aggressive editing such as mixing of other audio sources and source separation.
In this work, we propose for the first time a steganography method that can
embed information into individual sound sources in a mixture such as
instrumental tracks in music. To this end, we propose a time-domain model and
curriculum learning essential to learn to decode the concealed message from the
separated sources. Experimental results show that the proposed method
successfully conceals the information in an imperceptible perturbation and that
the information can be correctly recovered even after mixing of other sources
and separation by a source separation algorithm. Furthermore, we show that the
proposed method can be applied to multiple sources simultaneously without
interfering with the decoder for other sources even after the sources are mixed
and separated.
