Abstract This paper presents two adaptive and non-adaptive data hiding methods for grayscale images based on modulus function. Our adaptive scheme is based on the concept of human vision sensitivity, so the pixels in edge areas than to smooth areas can tolerate much more changes without making visible distortion for human eyes. In our adaptive scheme, the average differencing value of four neighborhood pixels into a block via a threshold secret key determines whether current block is located in edge or smooth area. Pixels in the edge areas are embedded by Q-bit of secret data with a larger value of Q than that of pixels placed in smooth areas. Also in this scholar, we represent one non-adaptive data hiding algorithm. Our non-adaptive scheme, via an error reduction procedure, produces a high visual quality for stego-image. The proposed schemes present several advantages. 1-of aspects the embedding capacity and visual quality of stego-image are scalable. In other words, the embedding rate as well as the image quality can be scaled for practical applications 2-the high embedding capacity with minimal visual distortion can be achieved, 3-our methods require little memory space for secret data embedding and extracting phases, 4-secret keys have used to protect of the embedded secret data. Thus, level of security is high, 5-the problem of overflow or underflow does not occur. Experimental results indicated that the proposed adaptive scheme significantly is superior to the currently existing scheme, in terms of stego-image visual quality, embedding capacity and level of security and also our non-adaptive method is better than other non-adaptive methods, in view of stego-image quality. Results show which our adaptive algorithm can resist against the RS steganalysis attack. Ó 2014 Production and hosting by Elsevier B.V. on behalf
In order to increase the performance, Maleki et al. REF present adaptive and non-adaptive schemes for grayscale images based on modulus functions.
Adaptive and non-adaptive data hiding methods for grayscale images based on modulus function
Egyptian Informatics Journal
Computer Science