Alpha-trimmed image estimation for JPEG steganography detection

In information security, steganalysis has been an important topic since evidences first indicated steganography has been used for covert communication. Among all digital files, numerous devices generate JPEG images due to the capability of compression and compatibility. A large number of JPEG steganography methods are also provided online for free usage. This has spawned significant research in the area of JPEG steganalysis. This paper introduces an image estimation technique utilizing the alpha-trimmed mean for distinguishing clean and steganography images. The hidden information is considered additive noise to the image. The alpha-trimmed method estimates steganographic messages within images in the spatial domain and provide flexibility for classifying various steganography methods in the JPEG compression domain. For three JPEG steganography methods along with three embedding message files applied to an image data set, the proposed method results in better separability between clean and steganographic classes. The results are based on comparisons between the presented method and two existing methods in which classification accuracies are increased by as much as 32%.