  Whereas cryptography easily arouses attacks by means of encrypting a secret
message into a suspicious form, steganography is advantageous for its
resilience to attacks by concealing the message in an innocent-looking cover
signal. Minimal distortion steganography, one of the mainstream steganography
frameworks, embeds messages while minimizing the distortion caused by the
modification on the cover elements. Due to the unavailability of the original
cover signal for the receiver, message embedding is realized by finding the
coset leader of the syndrome function of steganographic codes migrated from
channel coding, which is complex and has limited performance. Fortunately, deep
generative models and the robust semantic of generated data make it possible
for the receiver to perfectly reproduce the cover signal from the stego signal.
With this advantage, we propose cover-reproducible steganography where the
source coding, e.g., arithmetic coding, serves as the steganographic code.
Specifically, the decoding process of arithmetic coding is used for message
embedding and its encoding process is regarded as message extraction. Taking
text-to-speech and text-to-image synthesis tasks as two examples, we illustrate
the feasibility of cover-reproducible steganography. Steganalysis experiments
and theoretical analysis are conducted to demonstrate that the proposed methods
outperform the existing methods in most cases.
