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Impulse Technologies
                                      Beacons U to World of technology
        044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in
       Adaptive Steganalysis of Least Significant Bit Replacement in
                       Grayscale Natural Images
   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.




  Your Own Ideas or Any project from any company can be Implemented
at Better price (All Projects can be done in Java or DotNet whichever the student wants)
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  • 1. Impulse Technologies Beacons U to World of technology 044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in Adaptive Steganalysis of Least Significant Bit Replacement in Grayscale Natural Images 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. Your Own Ideas or Any project from any company can be Implemented at Better price (All Projects can be done in Java or DotNet whichever the student wants) 1