Eiji Kawaguchi

Principles and applications of BPCS steganography

Steganography is a technique to hide secret information in some other data without leaving any apparent evidence of data alternation. All of the traditional steganographic techniques have limited information-hiding capacity. They can hide only 10 percent of the data mounts of the vessel. This is because the principle of those techniques was either to replace a special part of the frequency components of the vessel image, or to replace all the least significant bits of a multi-valued image with the secret information.

BPCS steganography using EZW lossy compressed images

This paper presents a steganography method based on an embedded zerotree wavelet compression scheme and bit-plane complexity segmentation steganography. The proposed steganography enables us to use lossy compressed images as dummy files in bit-plane-based steganographic algorithms. Large embedding rates of around 25% of the compressed image size were achieved with little noticeable degradation in image quality.

High-performance JPEG steganography using quantization index modulation in DCT domain

This paper presents two JPEG steganographic methods using quantization index modulation (QIM) in the discrete cosine transform (DCT) domain. The two methods approximately preserve the histogram of quantized DCT coefficients, aiming at secure JPEG steganography against histogram-based attacks. In comparison with F5 as a representative JPEG steganography, the proposed methods show high performance with regard to embedding rate, PSNR of stego image, and particularly histogram preservation.

Application of bit-plane decomposition steganography to JPEG2000 encoded images

This letter presents a steganography method based on a JPEG2000 lossy compression scheme and bit-plane complexity segmentation (BPCS) steganography. It overcomes the lack of robustness of bit-plane-based steganography methods with respect to lossy compression of a dummy image: a critical shortcoming that has hampered deployment in a practical scenario. The proposed method is based on a seamless integration of the two schemes without compromising their desirable features and makes feasible the deployment of the merits of a BPCS steganography technique in a practical scenario where images are compressed before being transmitted over the network. Embedding rates of around 15% of the compressed image size were achieved for preembedding 1.0-bpp compressed images with no noticeable degradation in image quality.

MRF-based texture segmentation using wavelet decomposed images

In recent textured image segmentation, Bayesian approaches capitalizing on computational efficiency of multiresolution representations have received much attention. Most of the previous researches have been based on multiresolution stochastic models which use the Gaussian pyramid image decomposition. In this paper, motivated by nonredundant directional selectivity and highly discriminative nature of the wavelet representation, we present an unsupervised textured image segmentation algorithm based on a multiscale stochastic modeling over the wavelet decomposition of image. The model, using doubly stochastic Markov random fields, captures intrascale statistical dependencies over the wavelet decomposed image and intrascale and interscale dependencies over the corresponding multiresolution region image.

Peano scanning of arbitrary size images

Discrete space-filling curves are not uniquely defined. In addition to the condition that the curve must pass all the points of the array only once, continuously, it is necessary to add some critera to select the best curves. The authors aim is to preserve two-dimensional continuity as much as possible. The weighted sum of the distances of the points in the curve is minimized, where the weights are inversely proportional to the spatial distance between the points. However, the minimum is not unique. Particularly, space-filling curves always come on symmetric pairs. The generation of a near optimal space-filling curve is done hierarchically.<<ETX>>

A gray image compression using a Hilbert scan

Hilbert curve is one of the space-filling curves published by Peano. There are several applications using this curve such as image processing, computer hologram, etc. In this paper, we concentrate on a lossy compression technique for a gray image using the Hilbert curve. The merit of this curve is to pass through all points in a quadrant, and it always moves to the neighbor quadrant. Our method is based on this neighborhood property, by a simple segmentation of the scanned one-dimensional data using a zero order interpolation. From our experiments, we have confirmed that in spite of the simple computation in comparison to JPEG, acceptable quality images can be obtained at bit-rates above 0.6 bit/pixel.

Application of BPCS steganography to wavelet compressed video

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. 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.

Depth-first coding for multivalued pictures using bit-plane decomposition

A data compression technique using a bit-plane decomposition strategy of multivalued images is described. Although the bit-plane decomposition is mainly used for image transmission, our method takes the image expression for image database into consideration. It has two merits which are a hierarchical representation using depth-first (DF) expression and a simple noise reduction algorithm for the DF expression that is similar to human perception. The DF expression is useful for image expansion, rotation, etc. We study the information in an image that should be eliminated by noise reduction. Noise-like patterns in an image are uniformalized and the edge and smooth surfaces remain nearly unchanged. They are not blurred, but instead are a little enhanced. We also study the properties of the black-and-white (B/W) boundary points on bit-planes. The algorithm of the uniformalization process with a DF-expression of an image is described. An experiment for real image data is carried out by a comparison to other methods, and the results are discussed. >

Application of QIM with dead zone for histogram preserving JPEG steganography

This paper presents two histogram preserving JPEG steganographic methods aiming at secure JPEG steganography against histogram-based attacks. The first one is a histogram quasi-preserving method, which uses quantization index modulation (QIM) at quantization step of DCT coefficients. Since a straightforward application of QIM causes a significant histogram change, a device is introduced in order not to change the after-embedding histogram excessively. The second one is a histogram preserving method based on histogram matching using two quantizers with a dead zone. In comparison with F5 as a representative JPEG steganography, the two methods show high performance with regard to embedding rate, PSNR of stego image, and particularly histogram preservation.