Multivariate Side-Informed Gaussian Embedding Minimizing Statistical Detectability

Steganography schemes based on a deflection criterion for embedding posses a clear advantage against schemes based on heuristics as they provide a direct link between theoretical detectability and empirical performance. However, this advantage depends on the accuracy of the cover and stego model und...

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Bibliographic Details
Published inIEEE transactions on information forensics and security Vol. 17; pp. 1841 - 1854
Main Authors Giboulot, Eva, Bas, Patrick, Cogranne, Remi
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects
Computational modeling
Computer Science
Cost function
Covariance
Covariance matrices
Cryptography and Security
Discrete cosine transforms
Embedding
Engineering Sciences
hypothesis testing
Image compression
JPEG images
Mathematics
Pipelines
Signal and Image processing
statistical modeling
Statistical models
Statistics
Steganography
Transform coding
Steganography
statistical modeling
hypothesis testing
JPEG images
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Summary:Steganography schemes based on a deflection criterion for embedding posses a clear advantage against schemes based on heuristics as they provide a direct link between theoretical detectability and empirical performance. However, this advantage depends on the accuracy of the cover and stego model underlying the embedding scheme. In this work we propose an original steganography scheme based on a realistic model of sensor noise, taking into account the camera model, the ISO setting and the processing pipeline. Exploiting this statistical model allows us to take correlations between DCT coefficients into account. Several types of dependency models are presented, including a very general lattice model which accurately models dependencies introduced by a large class of processing pipelines of interest. We show in particular that the stego signal which minimizes the KL divergence under this model has a covariance proportional to the cover noise covariance. The resulting embedding scheme achieves state-of-the-art performances which go well beyond the current standards in side-informed JPEG steganography.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2022.3173184