Tag Archives: Image Steganalysis

Blind detection of low-rate embedding


Steganalysis of least significant bit (LSB) embedded images in spatial domain has been investigated extensively over the past decade and most well-known LSB steganography methods have been shown to be detectable. However, according to the latest findings in the area, two major issues of very low-rate (VLR) embedding and content-adaptive steganography have remained hard to resolve. The problem of VLR embedding is indeed a generic problem to any steganalyser, while the issue of adaptive embedding specifically depends on the hiding algorithm employed. The latter challenge has recently been brought up again to the area of LSB steganalysis by highly undetectable stego image steganography that offers a content-adaptive embedding scheme for grey-scale images. The authors new image steganalysis method suggests analysis of the relative norm of the image Clouds manipulated in an LSB embedding system. The method is a self-dependent image analysis and is capable of operating on low-resolution images. The proposed algorithm is applied to the image in spatial domain through image Clouding, relative auto-decorrelation features extraction and quadratic rate estimation, as the main steps of the proposed analysis procedure. The authors then introduce and use new statistical features, Clouds-Min-Sum and Local-Entropies-Sum, which improve both the detection accuracy and the embedding rate estimation. They analytically verify the functionality of the scheme. Their simulation results show that the proposed approach outperforms some well known, powerful LSB steganalysis schemes, in terms of true and false detection rates and mean squared error.


Image Steganalysis of Low Rate Embedding in Spatial Domain


LSB embedding in spatial domain with very low rate can be easily performed and its detection in spite of many researches is very hard, while BOSS competition has been held to break an adaptive embedding algorithm with low rate. Thus, proposing powerful steganalyzer of very low rate in spatial domain is highly requested. In this thesis it has been tried to present some algorithms to detect secret message with very low rate in spatial domain using eigenvalues analysis and relative auto-correlation of image.

First approach is based on the analysis of the eigenvalues of the cover correlation matrix that we used for the first time. Image partitioning, correlation function computation, constellation of the correlated data, and eigenvalues examination are major challenging parts of our analysis method. The proposed method uses the LSB plane of images in spatial domain, extendable to transform domain, for detecting low embedding rates that is a major concern in the area of the LSB steganography. Simulation results show that the proposed approach improves over some well-known LSB steganalysis methods, specifically at low embedding rates.

Our second image steganalysis method suggests analysis of the relative norm of the image parts manipulated in an LSB embedding system. Image partitioning, multidimensional cross-correlation, feature extraction, and rate estimation, as the major steps of the main analysis procedure. We then extract and use new statistical features, Parts-Min-Sum and Local-Entropies-Sum, to get a closer estimate of the embedding rate and the detection performance. Our simulation results, as compared to some recent steganographic methods show that our new approach outperforms some well-known, powerful LSB steganalysis schemes, in terms of true and false detection rates.

Keywords: Image Steganalysis, Eigenvalues Analysis, LSB Embedding, Relative Autocorrelation, Parts Min Sum, Embedding Rate Estimation, Local Entropies Sum.

Image Steganalysis of Low Bit-rate Embedding

SVD and Noise Estimation based Image Steganalysis

We propose a novel image steganalysis method, based on singular value decomposition and noise estimation, for the spatial domain LSB embedding families. We first define a content independence parameter, DS, that is calculated for each LSB embedding rate. Next, we estimate the DS curve and use noise estimation to improve the curve approximation accuracy. It is shown that the proposed approach gives an estimate of the LSB embedding rate, as well as information about the existence of the embedded message (if any). The proposed method can effectively be applied to a wide range of the image LSB steganography families in spatial domain. To evaluate the proposed scheme, we applied the method to a large image database. Using a large image database, simulation results of our steganalysis scheme indicate significant improvement to both true detection and false alarm rates.

Full text > SVD and noise estimation based image steganalysis