Using High-Dimensional Image Models to Perform Highly Undetectable Steganography

Tomas Pevny (1) , Tomas Filler (2) , Patrick Bas (3)

1 CTU - Czech Technical University in Prague
2 Binghamton University [SUNY]
3 LAGIS-SI

This paper presents a complete methodology for designing practical and highly-undetectable stegosystems for real digital media. The main design principle is to minimize a suitably-defined distortion by means of efficient coding algorithm. The distortion is defined as a weighted difference of extended state-of-the-art feature vectors already used in steganalysis. This allows us to "preserve" the model used by steganalyst and thus be undetectable even for large payloads. This framework can be efficiently implemented even when the dimensionality of the feature set used by the embedder is larger than 10^{7}. The high dimensional model is necessary to avoid known security weaknesses. Although high-dimensional models might be problem in steganalysis, we explain, why they are acceptable in steganography. As an example, we introduce HUGO, a new embedding algorithm for spatial-domain digital images and we contrast its performance with LSB matching. On the BOWS2 image database and in contrast with LSB matching, HUGO allows the embedder to hide 7\times longer message with the same level of security level.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Tomas Pevny, Tomas Filler, Patrick Bas. Using High-Dimensional Image Models to Perform Highly Undetectable Steganography. Information Hiding, Jun 2010, Calgary, Canada. pp.2010. ⟨hal-00541353⟩