Image disentanglement autoencoder for steganography without embedding

Ziping Ma, Xiyao Liu, Junxing Ma, Jian Zhang, Gerald Schaefer, Hui Fang

Abstract:

Conventional steganography approaches embed a secret
message into a carrier for concealed communication but
are prone to attack by recent advanced steganalysis tools.
In this paper, we propose Image DisEntanglement Autoencoder for Steganography (IDEAS) as a novel steganography
without embedding (SWE) technique. Instead of directly
embedding the secret message into a carrier image, our approach hides it by transforming it into a synthesised image,
and is thus fundamentally immune to typical steganalysis attacks. By disentangling an image into two representations
for structure and texture, we exploit the stability of structure
representation to improve secret message extraction while
increasing synthesis diversity via randomising texture representations to enhance steganography security. In addition, we design an adaptive mapping mechanism to further
enhance the diversity of synthesised images when ensuring different required extraction levels. Experimental results convincingly demonstrate IDEAS to achieve superior
performance in terms of enhanced security, reliable secret
message extraction and flexible adaptation for different extraction levels, compared to state-of-the-art SWE methods.