Implementation of Adaptive Steganalysis Of LSB Replacement in Colour Images

This paper deals with the detection of hidden bits in the Least Significant Bit (LSB) plane of a natural colour 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 preferred such that its probability distribution is always independent of the unknown image parameters, while ensuring a high probability of hidden bits detection. The pixel of the original image and the cover image will be changed and detected by the detection algorithm. 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.