Steganalysis

Exploit

Computer security

Theoretical computer science

Steganography

Computer science

Embedding

Steganography tools

Pattern recognition (psychology)

Politics

Image (mathematics)

Political science

Autoencoder

Artificial intelligence

Representation (politics)

Cryptography

Artificial neural network

Feature extraction

Computer vision

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.

Image Disentanglement Autoencoder for Steganography without Embedding