Steganalysis: Using the blind deconvolution to retrieve the hidden data

Steganography has gained a substantial attention due to its application in wide areas. Steganography as it literally mean is hiding the information (stego data) inside the data (communication data) so that the receiver can only extract the desired information from the data. Steganalysis is the reverse process of steganography in which the information about the original data is hardly available, from the received data the extractor needs to identify the original data. Since this belong to a class of inverse problems it is hard to find the approximate match of the original data from the received one. In most of the cases this will fall under the category of ill-posed problems. The stego-data that has been embedded into the communication data can be considered as linear bounded operator operating on the input data and the reverse process (the Steganalysis) can be thought like a deconvolution problem by which we can extract the original data. Here we are assuming the watermarking as a linear operation with a bounded linear operator K : X→Y where X and Y are spaces of Bounded Variation (BV). The forward problem (the Steganography) is a direct convolution and the reverse (backward) problem (steganalysis) is a de-convolution procedure. In this work we are embedding a Gaussian random variable array with zero mean and with a specific variance into the data and we show how the original data can be extracted using the regularization method. The results are shown to substantiate the ability of the method to perform steganalysis.