A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting LSB Steganography in Color and Gray-Scale Images
IEEE MultiMedia
Attacks on Steganographic Systems
IH '99 Proceedings of the Third International Workshop on Information Hiding
An Implementation of Key-Based Digital Signal Steganography
IHW '01 Proceedings of the 4th International Workshop on Information Hiding
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Detection of LSB steganography via sample pair analysis
IEEE Transactions on Signal Processing
On the limits of steganography
IEEE Journal on Selected Areas in Communications
Spread spectrum image steganography
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Stochastic modulation steganography hides secret message within the cover image by adding a weak noise signal with a specified probabilistic distribution. The advantages of stochastic modulation steganography include high capacity and better security. Current steganalysis methods that are applicable to the detection of hidden message in traditional least significant bit (LSB) or additive noise model based steganography cannot reliably detect the existence of hidden message in stochastic modulation steganography. In this paper, we present a new steganalysis approach which can reliably detect the existence and accurately estimate the length of hidden message in stochastic modulation steganography. By analyzing the distributions of the horizontal pixel difference of the images before and after stochastic modulation embedding, it is shown that for non-adaptive steganography, the distribution of the stego-image’s pixel difference can be modeled as the convolution of the distribution of the cover image’s pixel difference and that of the quantized stego-noise difference, and that the estimation of the hidden message length in stochastic modulation can be achieved by estimating the variance of the stego-noise. To estimate the variance of the stego-noise, hence determining the existence and the length of hidden message, we first model the distribution of the cover image’s pixel difference as a generalized Gaussian and estimate the parameters of the distribution using grid search and Chi-square goodness of fit test, and then exploit the relationship between the distribution variance of the cover image’s pixel difference and that of the stego-noise difference. We present experimental results to demonstrate that our new approach is effective for steganalyzing stochastic modulation steganography. Our method provides a general theoretical framework and is applicable to other non-adaptive embedding algorithms where the distribution models of the stego-noise are known or can be estimated.