Texture-based image steganalysis by artificial neural networks

Purpose - The purpose of this paper is to present research results in analyzing image contents to improve the accuracy of using an artificial neural network (ANN) to detect embedded data in a digital image. Design/methodology/approach - A texture measure based on the MPEG-7 texture descriptor is applied to assess the local texture amount. Those image blocks with high texture are masked out and the remaining blocks with low texture are used to derive features for an ANN to classify an image as embedded or clear. The high-texture blocks are not discarded and can be tested independently for embedded data. Findings - By masking out the high-texture image blocks, an ANN has improved detection performance especially when the original embedding rate is low. Bypassing the low-texture image blocks do not pay off for a steganographer because the effective embedding rate in the high-texture blocks is driven higher. Research limitations/implications - Hidden data detectors should take the image content into account in order to improve detection performance. Practical implications - The results can be integrated into a steganalytic system. Originality/value - This paper presents evidence that image texture affects steganalytic performance and proposes a solution that incorporates texture that has improved detection performance.