  Steganography conceals the secret message into the cover media, generating a
stego media which can be transmitted on public channels without drawing
suspicion. As its countermeasure, steganalysis mainly aims to detect whether
the secret message is hidden in a given media. Although the steganography
techniques are improving constantly, the sophisticated steganalysis can always
break a known steganographic method to some extent. With a stego media
discovered, the adversary could find out the sender or receiver and coerce them
to disclose the secret message, which we name as coercive attack in this paper.
Inspired by the idea of deniable encryption, we build up the concepts of
deniable steganography for the first time and discuss the feasible
constructions for it. As an example, we propose a receiver-deniable
steganographic scheme to deal with the receiver-side coercive attack using deep
neural networks (DNN). Specifically, besides the real secret message, a piece
of fake message is also embedded into the cover. On the receiver side, the real
message can be extracted with an extraction module; while once the receiver has
to surrender a piece of secret message under coercive attack, he can extract
the fake message to deceive the adversary with another extraction module.
Experiments demonstrate the scalability and sensitivity of the DNN-based
receiver-deniable steganographic scheme.
