EDGE ADAPTIVE IMAGE STEGANOGRAPHY BASED ON LSB MATCHING REVISITED PDF

However, we find that in most existing approaches, the choice of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the relationship between the image content itself and the size of the secret message. In this paper, we expand the LSB matching revisited image steganography and propose an edge adaptive scheme which can select the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For lower embedding rates, only sharper edge regions are used while keeping the other smoother regions as they are. When the embedding rate increases, more edge regions can be released adaptively for data hiding by adjusting just a few parameters. The experimental results evaluated on natural images with three specific and four universal steganalytic algorithms show that the new scheme can enhance the security significantly compared with typical LSB-based approaches as well as their edge adaptive ones, such as pixel-value-differencing-based approaches, while preserving higher visual quality of stego images at the same time.

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Sivaranjani 2Ms. If the secret bit does not match the LSB of popular type of steganographic algorithms in the spatial the domain. However, we find that in most existing approaches, the cover image, then or is randomly added to the corresponding choice of embedding positions within a cover image mainly pixel value.

Statistically, the probability of increasing or depends on a pseudorandom number generator without decreasing for each modified pixel value is the same and so considering the relationship between the image content itself and the size of the secret message.

Therefore, the common hiding even at a low embedding rate, and this will lead to poor approaches used to detect LSB replacement are totally visual quality and low security based on our analysis and ineffective at detecting the LSBM. Up to now, several extensive experiments, especially for those images with many steganalytic algorithms e.

Unlike LSB replacement and revisited image steganography and propose an edge adaptive LSBM, which deal with the pixel values independently, LSB scheme which can select the embedding regions according to the matching revisited LSBMR [1] uses a pair of pixels as an size of secret message and the difference between two embedding unit, in which the LSB of the first pixel carries consecutive pixels in the cover image.

For lower embedding rates, only sharper edge regions are used while keeping the one bit of secret message, and the relationship odd—even other smoother regions as they are. When the embedding rate combination of the two pixel values carries another bit of increases, more edge regions can be released adaptively for data secret message. In such a way, the modification rate of pixels hiding by adjusting just a few parameters. Keywords- can decrease from 0. It is also shown that such a new scheme can avoid the LSB replacement style asymmetry, and thus it should make the detection slightly more difficult than I.

The pixel-value differencing PVD -based hidden secret messages in those stego media. If there exists a scheme e. The larger guessing, the steganographic system is considered broken. In the difference, the larger the number of secret bits that can be this paper, we consider digital images as covers and embedded. Usually, PVD-based approaches can provide a investigate an adaptive and secure data hiding scheme in the larger embedding capacity.

Assuming that a cover image is spatial least-significant-bit LSB domain. LSB replacement made up of many no overlapping small sub images regions is a well-known steganographic method. In this embedding based on a predetermined rule, then different regions usually scheme, only the LSB plane of the cover image is overwritten have different capacities for hiding the message. Generally, with the secret bit stream according to a pseudorandom the regions located at the sharper edges present more number generator PRNG.

As a result, some structural complicated statistical features and are highly dependent on asymmetry never decreasing even pixels and increasing odd the image contents.

In this paper, we propose an edge pixels when hiding the data is introduced, and thus it is very adaptive scheme and apply it to the LSBMR-based method. Section II embedding rate using some reported steganalytic algorithms. Section III shows the details of data embedding and data extraction in our scheme. Section IV presents experimental results and discussions.

Finally, concluding remarks and future. Manuscript received May 14, The flow diagram of our proposed scheme is illustrated in Ms. Finally, it does some number of elements in the set of. For each unit , we perform the data hiding according to the following four cases. Please note that the parameters may be different for different ; image content and secret message.

In data extraction, the where and denote two secret bits to be embedded. The scheme first extracts the side information from the stego function is defined as. Based on the side information, it then does some thepixel pair after data hiding. After the above modifications, preprocessing and identifies the regions that have been used and may be out of , or the new difference may be less than the for data hiding. Finally, it obtains the secret message threshold.

In such cases,1 we need to readjust them as according to the corresponding extraction algorithm. The details of the data embedding and data extraction algorithms are as follows. Data Embedding Step 1: The cover image of size of is first divided into non Finally, we have overlapping blocks of pixels.

The resulting image is rearranged as a row vector Step 4: After data hiding, the resulting image is divided into by raster scanning. And then the vector is divided into non non overlapping blocks. The blocks are then rotated by a overlapping embedding units with every two consecutive random number of degrees based on.

The process is very pixels, where, assuming is an even number. Two benefits can similar to Step 1 except that the random degrees are opposite. First, it can prevent the Then we embed the two parameters into a preset region detector from getting the correct embedding units without the which has not been used for data hiding. The first one is the rotation key, and thus security is improved. Furthermore, block size for block dividing in data preprocessing; another is both horizontal and vertical edges pixel pairs within the the threshold for embedding region selection.

In all, only 7 cover image can be used for data hiding. Therefore, for a III. Let be the set of pixel pairs whose In this paper, an edge adaptive image steganographic scheme absolute differences are greater than or equal to a parameter t in the spatial LSB domain is studied.

Huang, and Y. In most previous [13] M. Goljan, J. Fridrich, and T. Singh, L. Singh, A. Singh, and K. Devi, hiding characteristics. Information and Communication Technology, which can first embed the secret message into the sharper Mar.

Swanson, B. Zhu, and A. Furthermore, it is expected that our adaptive Processing Workshop, Sep. Zhang and S. Yang, C. Weng, S. Wang, and H. Sun, [2] A. Westfeld and A. Information Hiding, , vol. Forensics Security, vol. Fridrich, M. Goljan, and R. Kharrazi, H. Sencar, and N. IEEE [4] S. Dumitrescu, X. Wu, and Z. Image Processing, Oct. Signal Process. Karunya University, in information — She [6] D. Harmsen and W. Data mining and Web Services. She has 1 year of experience —, Jun.

Huang, B. Li, and J. IEEE Int. Engineering during the year She — Li, T. Zeng, and B. At present she is an assistant professor in the Security, Oxford, U. Shi et al. Multimedia and Expo, Jul.

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Improved Algorithm of Edge Adaptive Image Steganography Based on LSB Matching Revisited Algorithm

Sivaranjani 2Ms. If the secret bit does not match the LSB of popular type of steganographic algorithms in the spatial the domain. However, we find that in most existing approaches, the cover image, then or is randomly added to the corresponding choice of embedding positions within a cover image mainly pixel value. Statistically, the probability of increasing or depends on a pseudorandom number generator without decreasing for each modified pixel value is the same and so considering the relationship between the image content itself and the size of the secret message. Therefore, the common hiding even at a low embedding rate, and this will lead to poor approaches used to detect LSB replacement are totally visual quality and low security based on our analysis and ineffective at detecting the LSBM. Up to now, several extensive experiments, especially for those images with many steganalytic algorithms e.

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