KEYWORDS: Steganalysis, Sensors, Cameras, Steganography, Signal to noise ratio, Detection and tracking algorithms, Signal detection, Digital imaging, Statistical analysis, Signal processing
We define sequential steganography as those class of embedding
algorithms that hide messages in consecutive (time, spatial or
frequency domain) features of a host signal. This paper presents a
steganalysis method that estimates the secret key used in
sequential steganography. A theory is developed for detecting
abrupt jumps in the statistics of the stego signal during
steganalysis. Stationary and non-stationary host signals with low,
medium and high SNR embedding are considered. A locally most
powerful steganalysis detector for the low SNR case is also
derived. Several techniques to make the steganalysis algorithm
work for non-stationary digital image steganalysis are also
presented. Extensive experimental results are shown to illustrate
the strengths and weaknesses of the proposed steganalysis
algorithm.
KEYWORDS: Steganography, Distortion, Digital watermarking, Data modeling, Multimedia, Steganalysis, Digital imaging, Computer programming, Matrices, Telecommunications
We analyze the complexity of the steganography problem and show that the decision version of the problem
is NP-complete through transformation from the Knapsack problem. We also give a pseudo-polynomial time
algorithm to optimally solve the steganography problem. This optimal algorithm can also be applied to image
steganography.
Steganalysis of sequential steganography is presented in this
paper. Abrupt change in statistics due to sequential embedding is
exploited by the proposed technique. Analytical derivations are
presented for several cases along with experimental results.
Experiments show that the proposed method can be used quite
effectively to detect locations and length of messages embedded
using spread spectrum steganography.
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