Steganography is a technique to hide secret information in some other data (we call it a vessel) without leaving any apparent evidence of data alteration. All of the traditional steganographic techniques have limited information-hiding capacity. They can hide only 10% (or less) of the data amounts 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 multivalued image with the secret information.Our new steganography uses an image as the vessel data, and we embed secret information in the bit-planes of the vessel. This technique makes use of the characteristics of the human vision system whereby a human cannot perceive any shape information in a very complicated binary pattern. We can replace all of the "noise-like" regions in the bit-planes of the vessel image with secret data without deteriorating the image quality. We termed our steganography "BPCS-Steganography," which stands for Bit-Plane Complexity Segmentation Steganography.We made an experimental system to investigate this technique in depth. The merits of BPCS-Steganography found by the experiments are as follows.I The information hiding capacity of a true color image is around 50%. 2. A sharpening operation on the dummy image increases the embedding capacity quite a bit. 3. Canonical Gray coded bit planes are more suitable for BPCS-Steganography than the standard binary bit planes. 4. Randomization of the secret data by a compression operation makes the embedded data more intangible. 5. Customization of a BPCS-Steganography program for each user is easy. It further protects against eavesdropping on the embedded information.

There have been many applications of the Hilbert curve, such as image processing, image compression, computer hologram, etc. The Hilbert curve is a one-to-one mapping between N-dimensional space and one-dimensional (l-D) space which preserves point neighborhoods as much as possible. There are several algorithms for N-dimensional Hilbert scanning, such as the Butz algorithm and the Quinqueton algorithm. The Butz algorithm is a mapping function using several bit operations such as shifting, exclusive OR, etc. On the other hand, the Quinqueton algorithm computes all addresses of this curve using recursive functions, but takes time to compute a one to-one mapping correspondence. Both algorithms are complex to compute and both are difficult to implement in hardware. In this paper, we propose a new, simple, nonrecursive algorithm for N-dimensional Hilbert scanning using look-up tables. The merit of our algorithm is that the computation is fast and the implementation is much easier than previous ones.

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This paper proposes a method ro apply BPCSSteganography that we have already proposed f o r gray scale images to palette-based images which consists of a palette storing color vector information and an index image whosepixel value is corresponding to a index in the palette.A palerre-based images can be represented by combining R G and B color componenr images. We embed secrer information into the G images. A number of color vectors in a palerre a f e r embedding by BPCS would be over the maximum numbec which is usually 256. In order to reduce the number of colors, the rest fwo component images are then changed in a way that minimizes the square errol: The idea behind the color quantization is rhar the degrading of images manipulated ro reduce color is worse than rhe degrading which occurs wirh the embedding.