Research On Algorithms Of Image-based Information Hiding And Steganalysis

With the rapid development of computer network technology, information security has attracted more and more attention. As two important branches of information security, information hiding and steganalysis, which restrict and promote manully, have experienced more than ten years of development. Firstly, we introduce the basic theory and development status of information hiding and steganalysis in this dissertation. Then, we focuse on the lossless data hiding technology and universal steganalysis schemes in digital images, as well as the practical applications of steganalysis. The main contents of this thesis are summarized as follows:(1) A lossless data hiding algorithm LDHS_DHS is proposed based on histogram shifting of adjacent pixel difference, which uses the original raw image as the cover object. We calculate the histogram of adjacent pixel differences in row direction or column direction, and can find that it presents as a Laplace-like distribution, and most elements concentrated near zero. If we shift the histogram peak, lots of redundant positions can be generated and utilized to embed secret messages losslessly. The proposed scheme can be implemented easily with low computational complexity. The alteration of pixel value caused by hisgotram shifting is at most 1, so the generated stego image has good quality. Furthermore, this algorithm can be simply extended to embed much more hidden messages losslessly. Experimental results show that the average payload among eight typical grayscale images with the resolution of 512×512 can achieve up to 0.91bpp, where the stego images after data embedding still have good quality with the PSNR larger that 30dB. The proposed scheme can provide larger embedding capacity than some traditional schemes.(2) A blind detection algorithm BS_DSF for JPEG images with features extracted from DCT and spatial domain is proposed. Firstly, a series of one-or two-order statistics are constructed to capture the changes of statistical characteristics occurred in the DCT coefficients and pixel values caused by data embedding. The statistics are composed of DCT statistics and spatial statistics. The former include histogram of global AC coefficients, histogram of individual coefficients, histogram of coefficients with specified value, histogram of adjacent DCT coefficient differences, co-occurrence matrices of coefficients and coefficnet differences between adjacent DCT blocks; while the latter include histogram of global pixels, histogram of pixels along DCT block boundaries and co-occurrence matrix of adjacent pixel differences. In order to capture more information about embedding changes, for each statistic, we extracte features in macroscopic and microscopic perspective, respectively. We calculate the high order statistical moments in the frequency domain as the macroscopic features, and select the individual elements which may hold main information as the microscopic features, and then, a 194-dimensional feature vector is formd. Support vector machines are used to build classifiers in experiments on Greenspun image library. The results demonstrate that the proposed scheme can provide better detection accuracy than some traditional blind detection schemes for JPEG images.(3) A blind steganalytic method BS_MDF is proposed with features extracted from DCT, DWT and spatial domain using joint probability density matrix. Firstly, we calculate the intra-block and inter-block correlations of DCT coefficients using joint probability density matrix as the DCT features. Then, we use one-level Haar wavelet decomposition to the test image, and calculate the joint probability density of low frequence wavelet coefficients as the DWT features. At last, we decompress and preprocess the test image into spatial domain, calculate the joint probability density matrices of pixel values on test image and calibrated image, and take the difference as the spatial features. Feature vector is formed by DCT, DWT and spatial features. A series of experiments are made for five kinds of typical JPEG steganography methods in three different image libraries (Coreldraw, Greenspun and UCID.v2). Results demonstrate that the BS_MDF algorithm can provide reliable detection accuracy. Furthermore, experimental results show that feature reduction is an important step in the blind detection framework, which can improve the accuracy and decrease the time of classification greatly.(4) A network-based monitoring and detection system for hidden information is designed and implemented in this thesis. The whole system consists of three subsystems:image capture system on network, hidden information detection engine and central database. The image capture device captures the packets flowing through the network card of the host machine, and assembles them into digital images. The main work of hidden information detection engine is to determine whether the captured image has hidden messages embedded, and distinguish which steganograhpy has been used if the image is considered as stego image. Central database revieces and stores the relevant information of suspicious images transmitted from the detection engine, and presents the results of statistical analysis according to different users'demands through a web browser. The proposed system can be extended simply to adapt to actual network environment, and a weighted machanism is used in detection engine for the purpose of decreasing the false alarm rate, which is very important in actual requirements. This work has done a preliminary attempt in the practical application of steganalysis, and is of great significance.