Research On Steganorgraphy And Steganalysis Of Digital Image

With the development of network and information technology, digital media data can be transmitted fleetly and conveniently on communication netwok. Bring conveniency to communication, it also brings new challenge to communication security. Information hiding has become the hotspot in reseach of communication security. Steganography, as the main embranchment of information hiding, hiding the secret information into open digital media for the purpose of secret communication, is more imperceptible. Research on steganography and the detection of hiding message is very important to national communication security. This thesis is mainly researching on steganography and steganalysis technique of images.A review of steganorgraphy in images is given. The security of steganography system is analysised using information theory. A steganography algorithm in DCT domain of image is proposed. Using literal image as secret message and still image as cover, this algorithm combines image scrambling with digital modulation. Experiment results show that the property of invisibility and anticropping is good. Based on human visual system model, a novel image steganography algorithm in wavelet domain is proposed using the iterative blending method. Giving the character of human visual system, a JND (just noticed difference) threshold matrix is caculated pixel by pixel in the image subbands. Using the iterative blending method, the secret message is embedded into wavelet coefficients adaptively. Experiment results show that the property of invisibility and robustness is good.A review of steganalysis in images is given. Steganography software Jsteg uses the LSB of DCT coefficients to hide secret message. The tranditional Chi-square test can only detec the sequential Jsteg hiding and can not detect the random Jsteg hiding. A new fast steganalysis algorithm for detecting Jsteg hiding based on statistical model of image is proposed. The algorithm uses the Laplacian to fit the distribution of statistic of AC coefficients and uses Pearsonχ~2 test to test goodness-of fit. The amount of embedding message is estimated using linear regression. The algorithm can detect both sequential Jsteg hiding and random Jsteg hiding.The kind of steganography algorithms using the least significant bit of the DCT coefficients to hide message have the characters of invisibility and robustness. This kind of steganography algorithms includes Jsteg, Outguess and so on. A steganalysis algorithm that can detect the hiding in the least significant bit of the DCT coefficients is proposed. The algorithm is based on the thought that the DCT coefficients are correlative. Using the statistical tests as the features, support vector regression is trained to discriminate the stego-images from the clear ones. Experiment results show that our method can detect the hiding by Jsteg and OutGuess. The purpose of active steganalysis is not only detecting stego-image, but also estimate the parameter of embedding algorithm for extraction of the secret message. A steganalysis algorithm that estimates the secret key used in sequential steganography of spread spectrum embedding is proposed. Considering the Laplacian distribution of image DCT coefficients, a secret key estimation model of stationary laplacian host signal embedded by spread spectrum steganography is presented based on the theory of detection of abrupt changes and sequential detection. For non-statioanry digital image, a locally most powerful steganaysis detector is derived based on Laplacian distribution. The results of experiments show that our method is more efficient than Trivedi's method.An image steganalysis software is designed and implemented by Visual C++ language. This software can detects the steganography of connecting, hiding in spacial domain, hiding in frequent domain and hiding using some steganography software.A sumarise of steganography in test is given. By deeply study the mechanism of Stego, two steganalysis methods are proposed. When the first letters of words in dictionary are lowercases, the steganalysis method based on the signature characters can detect Stego. When it's not the case, the steganalysis method based on statistical characters can detect Stego.