Quantitative steganalysis of spatial ±1 steganography in JPEG decompressed images

Abstract

On the basis of the analysis of JPEG error and stegonoise, we propose a novel quantitative steganalyzer for spatial ±1 steganography in JPEG decompressed images. First, we present a particular theoretical argument that the cover images, which are originally stored in JPEG format, can be approximately estimated through JPEG recompression with the detected quantization table. Then, on the basis of the relationship between the message embedding rate and the variance of the stegonoise in the discrete cosine transformation (DCT) domain, we construct a polynomial regression model to estimate the secret message length. The extensive experimental results show that the proposed scheme is computationally feasible and that it significantly outperforms the existing state-of-the-art estimators, especially for the images with high quality factors and embedding rates. The order of magnitude of the prediction error using the proposed scheme can remain in the 10?4 range, as measured by the median absolute difference. Moreover, our estimator is stable and robust with respect to the embedding rate and quality factor.