Hybrid weighted-stego detection using machine learning

This paper deals with stego-image steganalysis to detect hidden information in natural images. Hidden bits are embedded by using the Least Significant Bit (LSB) replacement mechanism. We address the problem of learning the weights which characterize the structure and the performance of the standard Weighted Stego-image (WS) detector. In this paper we propose a new Hybrid Weighted Stego-detection (HWS) algorithm. We assume that the WS weights are related to the image pixels variance through an unknown function which is decomposed onto a set of known basis functions. This yields a linear detector which consists of a linear combination of parametric features derived from the structure of the standard WS detector. The coefficients of the linear combination are learnt by minimizing calibrated losses using stochastic gradient descent or a more efficient stochastic Newton descent approach. Thus, the HWS algorithm benefits from two fundamental advantages: the posterior probability of detection is well estimated and the numerical complexity of the algorithm is linear with the number of samples and the dimension of the features. The benchmark on real images shows that HWS method outperforms standard WS baseline method.