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To receive news and publication updates for Advances in Multimedia, enter your email address in the box below. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Bit-plane complexity segmentation BPCS steganography is advantageous in its capacity and imperceptibility.

The important step of BPCS steganography is how to locate noisy regions in a cover image exactly. The regular method, black-and-white border complexity, is a simple and easy way, but it is not always useful, especially for periodical patterns.

Run-length irregularity and border noisiness are introduced in this paper to work out this problem. Conjugation operation is applied to convert simple blocks into complex ones. The higher the bit-plane is, the smaller the complexity is. Steganography is an art and science of invisible communication. Embedding capacity, security imperceptibility , and robustness are the most important three features in steganographic system.

Generally speaking, there is a fundamental compromise between embedding capacity and security in information hiding scheme. Steganography methods can be divided into two categories: Cover image is transformed into frequency domain first, and then the secret messages are embedded in the coefficients of transformed cover image.

One was image enhancement and the other was image steganography based on DFT. Kaur and Kochhar [ 3 ] provided a technique for image steganography based on DCT. The algorithm had good invisibility and security. A three-level DWT decomposition was done on host image and secret information was hidden in approximation coefficients of the decomposed image in [ 4 ].

The stegoimage is subjected to image processing attacks. The optimal pixel adjustment process and genetic algorithm are also used. Secret data was embedded in high frequency directional band pass of contourlet transform. In spatial domain methods, the processing is applied on the image pixel values directly. Least significant bits LSB substitution [ 8 ] is the most well-known spatial steganographic system.

Although this method is simple and easy, it is weak in robustness and compression, such as JPEG compression. Mandal and Das [ 10 ] proposed a color image steganography method based on pixel value differencing. Secret data was hiding in the component of a pixel in a color image. Arora and Anand [ 11 ] gave an approach for image steganography using edge detection method. In their study, edges of RGB image were detected by scanning method using 3 3 window.

Since human eyes are not sensitive to tiny alterations of noisy data, it will not be noticed when the data in noisy regions is replaced with another noisy data. That is bit-plane complexity segmentation steganography BPCS steganography [ 12 ].

In LSB technique, data is hidden in the lowest bit-plane. But in BPCS technique, data is hidden in pixel blocks of all the planes, from the highest plane most significant bit, MSB plane to the lowest plane LSB plane , which have noisy patterns [ 15 ]. In BPCS, a gray image consisting of -bit pixels can be decomposed into -binary planes.

For example, , as shown in Figure 1. For example, is an -bit gray image; here. It is simple in informative region and cannot be used for hiding information. However, it is complex in noise-like region and each noise-like region could be replaced with another noise-like pattern in BPCS. As a result, it will not change the overall quality of image after embedding.

The most important step in BPCS is how to locate noisy regions in a cover image correctly. The regular method is to divide each bit-plane of the cover image into small square binary pixel blocks. The blocks are considered as noisy regions; those have complex black-and-white patterns. Often is defined as a criterion to judge whether the block is complex or not [ 16 ]: If is higher than the given threshold value, then the block is regarded as complex. The black-and-white border complexity is a simple and easy method to judge whether the blocks are complex or not.

However it is not always useful. For example, the blocks that have periodical patterns, such as chessboard or stripes, are recognized as complex ones in this way. That is because, as shown in Figure 2 , and. But these blocks cannot be used for embedding data; otherwise the cover image will deteriorate obviously.

There are two new techniques to differentiate complex blocks from simple ones in this paper: Run-length irregularity is the histogram which consists of the run-lengths of both black-and-white pixels in a row or in a column.

Suppose that is the frequency of runs of pixels either in black or in white and is the length of the pixel sequence; then is used to measure the irregularity of a binary pixel sequence: The value of is often normalized to and denoted by. If the size of block is and and are the th row and th column of a block, then the run-length irregularity of a block is defined as follows: According to the definition, the smaller row and column averages are taken as the value of the run-length irregularity.

As seen in Figure 3 , they are both periodic in row or column. As a result, every run-length irregularity is 0, so they are simple and cannot be used for embedding. The run-length irregularity is only useful in row or column.

If the block is regular in other directions, will have nothing to do with it, as shown in Figure 3. If data is embedded on the boundary of noisy regions and informative regions of cover image blocks, then the noisy regions will be expended.

As a result, the cover image will be changed clearly. The border noisiness is based on the differences between adjacent binary pixel sequences in a block. Similarly if the block size is and and are the th row and th column of a block, then the border noisiness of a block is defined as follows: The border noisiness is used to check if many black-and-white pixel borders are well distributed over a block along both the horizontal and the vertical directions.

So they are not complex according to the border noisiness and are not suitable for embedding. In a word, a block is recognized as complex only if it satisfies the following conditions: Here, , , and are threshold values.

Normally, the default threshold values are From the equation above, it can be concluded that the bit-plane is higher and the threshold value is larger. That is because it will raise noticeable changes for excessive embedding data in high bit-plane and will freely get enough embedding capacity in low bit-plane. There is a fact that these threshold values are default and not always optimal.

They could be adjusted manually according to the actual conditions. Normally the pixel value of an image is represented by pure binary code PBC. CGC makes full use of characteristic of human vision system and works out this problem. Suppose that and are the th bit of the pure binary code and canonical Gray code with the same -bit. The two codes are given by The relationship between and is given: Here means exclusive OR operation.

It consists of informative and noise-like regions in binary image. Informative regions are simple while noise-like regions are complex. If secret information is noise-like then it is embedded in noise-like regions of the cover image directly. However, if secret data is informative then it will be conjugated firstly so as to convert it into complex pattern. Conjugation is used to convert simple blocks into complex ones. In order to extract original data embedded in the cover image, it should record which blocks of the original data are conjugated and which are not.

This information is stored in a conjugation map. It should be embedded along with the resource file as blocks. Note that the blocks of the conjugation map should be also conjugated if they are not complex enough. If is a binary image with size of and black pixel is its foreground while white pixel is its background.

Now two checkerboard patterns and are put forward. In particular, has a white pixel at the upper-left position, and is its complement. In binary image black-and-white pixels particularly mean a logical value of 1 and 0. Now is defined as the conjugate of which satisfies The most important property about conjugation is This property could transform informative pattern to complex one, so both informative and noise-like regions could be used for embedding by conjugation operation.

Though BPCS steganography algorithm is advantageous in imperceptibility, it would change the statistic characteristics of bit-plane block complexity.

Because adjacent pixels in the high bit-planes have a strong correlation, the higher the bit-plane is, the stronger the correlation is. The method of steganalysis is proposed by Zhang and Wang [ 17 ].

If is not as big as the threshold value , the blocks should be done by conjugation first. Finally is the complexity histogram of all the bit-plane blocks of cover image in which secret data has been embedded, and is defined as follows: From 14 , it can be concluded that is composed of three parts of complexity histograms; each of them is smooth connection, but there is a sharp jump at the joint of two adjacent histograms, such as and.

The way of steganalysis is shown as follows: If there are obvious peak values which are larger than 0 in the , then the cover image will be considered as embedding secret information. In order to resist BPCS statistical analysis, the basic principle of improved BPCS steganography algorithm adopted different bit-plane with different complexity, which the higher is, the complexity smaller is. The threshold value of bit-plane complexity is computed in the same way as in Section 3.

The block size is , and the peak signal-to-noise ratio PSNR and capacity are used to evaluate the quality of the cover image after embedding: