KEYWORDS: Steganography, Wavelets, Video compression, Video, 3D video compression, Image compression, Binary data, JPEG2000, Computer programming, 3D image processing
This paper presents a steganography method using lossy compressed video which provides a natural way to send a large amount of secret data. The proposed method is based on wavelet compression for video data and bit-plane complexity segmentation (BPCS) steganography. BPCS steganography makes use of bit-plane decomposition and the characteristics of the human vision system, where noise-like regions in bit-planes of a dummy image are replaced with secret data without deteriorating image quality. In wavelet-based video compression methods such as 3-D set partitioning in hierarchical trees (SPIHT) algorithm and Motion-JPEG2000, wavelet coefficients in discrete wavelet transformed video are quantized into a bit-plane structure and therefore BPCS steganography can be applied in the wavelet domain. 3-D SPIHT-BPCS steganography and Motion-JPEG2000-BPCS steganography are presented and tested, which are the integration of 3-D SPIHT video coding and BPCS steganography, and that of Motion-JPEG2000 and BPCS, respectively. Experimental results show that 3-D SPIHT-BPCS is superior to Motion-JPEG2000-BPCS with regard to embedding performance. In 3-D SPIHT-BPCS steganography, embedding rates of around 28% of the compressed video size are achieved for twelve bit representation of wavelet coefficients with no noticeable degradation in video quality.
BPCS-Steganography is a steganographic method that hides secret messages in digital images. BPCS-Steganography extracts local regions of the image to embed using image segmentation based on a complexity measure that separates the image into ``informative'' and ``noise-like'' regions. The human visual system will be unable to perceive any difference by the replacement of noise regions with random binary data. This property allows us to embed secret data into such noise-like regions if the secret data is a random pattern. To avoid suspicion, an image should look innocent after embedding with secret information, not only visually, but also by analysis. A complexity histogram represents the relative frequency of occurrence of the various complexities. In previous work, we studied the complexity histogram of an image when embedded with secret data using BPCS-Steganography, and pointed out an anomaly in its shape. In this paper, we analyze other features of the image theoretically and practically. We consider the intensity of pixels in color components and luminance and analyze the shape of those histograms. As the result of the experiments, we show a more secure method to embed by BPCS-Steganography.
Internet bandwidth is in high demand, and one way that web sites lower the amount of bandwidth they use is by compressing their site's images. This lowers the amount of bandwidth used, and makes the site load much faster. There are of course many other useful applications for compressed images. Bit Plane Complexity Segmentation (BPCS) digital picture steganography is a technique to hide data inside an image file. BPCS achieves high embedding rates with low distortion based on the theory that noise-like regions in a bit-plane can be replaced with noise-like secret data without discernible loss in image quality. This is possible because the human eye, while very good at distinguishing anomalies in areas of homogenous texture, is bad at distinguishing anomalies in visually complex areas. However, BPCS is not a robust embedding scheme, and any lossy compression usually destroys the data. Wavelet image compression using the Discreet Wavelet Transform (DWT) is the basis of many modern compression schemes. The coefficients generated by certain wavelet transforms have many image-like qualities. These qualities can be exploited to allow BPCS to be performed on the coefficients. The results can then be losslessly encoded, combining the good compression of the DWT with the high embedding rates of BPCS.
Image index tables values generally give the best possible representation of the color information of the image. However, no consideration is given to the arrangement of the color table itself. Thus, depending on the image, pixels with similar colors may have different index values and can therefore have considerably different index binary makeups. Consequently, regions of similarly colored indexed pixels can be noise-like at the bitplane level while the output colors themselves may imply simple bitplane patterns. BPCS image steganography hides information in images based on the principle that if regions in a bitplane are noise-like, those regions can be replaced with noise-like secret data. Therefore, applying traditional BPCS steganography to indexed image data results in drastic visible changes to the image. To overcome this problem, we used a self-organizing neural network to reorder the index table, based on samples from the image, such that similar colors in the index table are near each other with respect to their index values. As a result, regions with similar color information have only slight binary differences at the bitplane level, whereas regions with mixed color information will have considerable binary differences. Using this technique, we can embed secret data that is 15 to 35 percent the size of the image with little or no noticeable degradation in the image.
One difficulty of textured image segmentation in the past was the lack of computationally efficient models which can capture the statistical regularities of textures over large distances. Recently, to overcome this difficulty, Bayesian approaches capitalizing on the computational efficiency of multiresolution representations have received attention. Most of the previous researches have been based on multiresolution stochastic models which use the Gaussian pyramid decomposition as the image decomposition scheme. In this paper, motivated by the nonredundant, directional selectivity, and highly discriminative nature of the wavelet representation, we present an unsupervised textured image segmentation algorithm which is based on a multiscale stochastic modeling over the wavelet decomposition of the image. The model, using doubly stochastic Markov random fields (MRFs), captures intrascale statistical dependencies over the observed image's wavelet decomposition and intrascale and interscale statistical dependencies over the corresponding multiresolution region image (an unobserved image which contains the classification of pixels in the image). For the sake of computational efficiency, versions of the Expectation-Maximization (EM) algorithm and Maximum a posteriori (MAP) estimate, which are based on the mean-field decomposition of a posteriori probability, are used for estimating model parameters and the segmented image, respectively.
This paper proposes a Markov random field (MRF) model-based method for unsupervised segmentation of images consisting of multiple textures. To model such textured images, a hierarchical MRF is used with two layers, the first layer representing an unobservable region image and the second layer representing multiple textures which cover each region. This method uses the Expectation and Maximization (EM) method for model parameter estimation, where in order to overcome the well-noticed computational problem in the expectation step, we approximate the Baum function using mean-field-based decomposition of a posteriori probability. Given provisionally estimated parameters at each iteration in the EM method, a provisional segmentation is carried out using local a posteriori probability (LAP) of each pixel's region label, which is derived by mean-field-based decomposition of a posteriori probability of the whole region image. Simulation results show that the use of LAPs is essential to perform a good image segmentation.
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