NDLI: Adaptive Steganalysis of Least Significant Bit Replacement in Grayscale Natural Images

Content Provider	IEEE Xplore Digital Library
Author	Fillatre, L.
Copyright Year	1991
Abstract	This paper deals with the detection of hidden bits in the Least Significant Bit (LSB) plane of a natural image. The mean level and the covariance matrix of the image, considered as a quantized Gaussian random matrix, are unknown. An adaptive statistical test is designed such that its probability distribution is always independent of the unknown image parameters, while ensuring a high probability of hidden bits detection. This test is based on the likelihood ratio test except that the unknown parameters are replaced by estimates based on a local linear regression model. It is shown that this test maximizes the probability of detection as the image size becomes arbitrarily large and the quantization step vanishes. This provides an asymptotic upper-bound for the detection of hidden bits based on the LSB replacement mechanism. Numerical results on real natural images show the relevance of the method and the sharpness of the asymptotic expression for the probability of detection.
Sponsorship	IEEE Signal Processing Society
Starting Page	556
Ending Page	569
Page Count	14
File Size	537548
File Format	PDF
ISSN	1053587X
Volume Number	60
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quantization Vectors Detectors Testing Approximation methods Probability Joints statistical hypotheses testing Adaptive detection information hiding natural image nuisance parameters
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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