Content-Adaptive Steganography by Minimizing Statistical Detectability

• Published in: IEEE transactions on information forensics and security Vol. 11; no. 2; pp. 221 - 234
• Main Authors: Sedighi, Vahid, Cogranne, Remi, Fridrich, Jessica
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Adaptive steganography and steganalysis; Classifiers; Computer information security; Detectors; Distortion; Empirical analysis; Encoding; Engineering Sciences; Exact solutions; hypothesis testing theory; information hiding; Mathematical analysis; multivariate Gaussian; Noise; optimal detection; Payloads; Random variables; Security; optimal detection; Adaptive steganography and steganalysis; hypothesis testing theory; multivariate Gaussian; information hiding
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2015.2486744

Most current steganographic schemes embed the secret payload by minimizing a heuristically defined distortion. Similarly, their security is evaluated empirically using classifiers equipped with rich image models. In this paper, we pursue an alternative approach based on a locally estimated multivariate Gaussian cover image model that is sufficiently simple to derive a closed-form expression for the power of the most powerful detector of content-adaptive least significant bit matching but, at the same time, complex enough to capture the non-stationary character of natural images. We show that when the cover model estimator is properly chosen, the state-of-the-art performance can be obtained. The closed-form expression for detectability within the chosen model is used to obtain new fundamental insight regarding the performance limits of empirical steganalysis detectors built as classifiers. In particular, we consider a novel detectability limited sender and estimate the secure payload of individual images.