Least significant bit steganography detection with machine learning techniques
Proceedings of the 2007 international workshop on Domain driven data mining
ISC '08 Proceedings of the 11th international conference on Information Security
Steganalysis of halftone image using inverse halftoning
Signal Processing
A hypothesis testing approach to semifragile watermark-based authentication
IEEE Transactions on Information Forensics and Security
YASS: yet another steganographic scheme that resists blind steganalysis
IH'07 Proceedings of the 9th international conference on Information hiding
A fusion of maximum likelihood and structural steganalysis
IH'07 Proceedings of the 9th international conference on Information hiding
Quantitative steganalysis of LSB embedding in JPEG domain
Proceedings of the 12th ACM workshop on Multimedia and security
Quantitative structural steganalysis of Jsteg
IEEE Transactions on Information Forensics and Security
Statistical decision methods in hidden information detection
IH'11 Proceedings of the 13th international conference on Information hiding
A cover image model for reliable steganalysis
IH'11 Proceedings of the 13th international conference on Information hiding
Assessment of steganalytic methods using multiple regression models
IH'05 Proceedings of the 7th international conference on Information Hiding
An analysis of empirical PMF based tests for least significant bit image steganography
IH'05 Proceedings of the 7th international conference on Information Hiding
Statistical detection of LSB matching using hypothesis testing theory
IH'12 Proceedings of the 14th international conference on Information Hiding
Wiretap-proof: what they hear is not what you speak, and what you speak they do not hear
Proceedings of the 4th ACM conference on Data and application security and privacy
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In this paper, we apply the theory of hypothesis testing to the steganalysis, or detection of hidden data, in the least significant bit (LSB) of a host image. The hiding rate (if data is hidden) and host probability mass function (PMF) are unknown. Our main results are as follows. a) Two types of tests are derived: a universal (over choices of host PMF) method that has certain asymptotic optimality properties and methods that are based on knowledge or estimation of the host PMF and, hence, an appropriate likelihood ratio (LR). b) For a known host PMF, it is shown that the composite hypothesis testing problem corresponding to an unknown hiding rate reduces to a worst-case simple hypothesis testing problem. c) Using the results for a known host PMF, practical tests based on the estimation of the host PMF are obtained. These are shown to be superior to the state of the art in terms of receiver operating characteristics as well as self-calibration across different host images. Estimators for the hiding rate are also developed.