Efficient Data Embedding Technique Comparison Based On LSB Replacement and Pixel Pair Matching Method

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 quan tized Gaussian random matrix, are unknown. An adaptive statis tical 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 re placed 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 van ishes. This provides an asymptotic upperbound for the detection of hidden bits based on the LSB replacement mechanism. Numer ical results on real natural images show the relevance of the method and the sharpness of the asymptotic expression for the probability of detection.