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Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. 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.
Article :. Date of Publication: 17 February DOI: Need Help?
JOAO PERNAMBUCO SONS DE CARRILHOES PDF
IMAGE STEGANOGRAPHY BASED ON LSB MATCHING TECHNIQUE USING SECRET SHARING METHOD
To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Edge adaptive image steganography based on LSB matching revisited. Usually, the larger payload embedded in a cover, the more of embedding positions within a cover image mainly depends on a pseudorandom number generator without considering the detectable artifacts would be introduced into the stego.
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Improved Algorithm of Edge Adaptive Image Steganography Based on LSB Matching Revisited Algorithm
Springer Professional. Back to the search result list. Table of Contents. Hint Swipe to navigate through the chapters of this book Close hint. Abstract In edge adaptive image steganography based on LSB matching revisited algorithm EAMR for short in this paper , the secret message bits are embedded into those consecutive pixel pairs whose absolute difference of grey values are larger than or equal to a threshold T. In this paper, we propose an improved algorithm for EAMR, in which the adjacent pixel pairs for data hiding are selected in a new random way. Thus the attackers cannot locate the pixel pairs selected for data hiding accurately, and the abnormality that exists in HADPP cannot be discovered any longer.
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In edge adaptive image steganography based on LSB matching revisited algorithm EAMR for short in this paper , the secret message bits are embedded into those consecutive pixel pairs whose absolute difference of grey values are larger than or equal to a threshold T. In this paper, we propose an improved algorithm for EAMR, in which the adjacent pixel pairs for data hiding are selected in a new random way. Thus the attackers cannot locate the pixel pairs selected for data hiding accurately, and the abnormality that exists in HADPP cannot be discovered any longer. The authors would like to thank Dr. Skip to main content.