Reversible data hiding is a technique not only the secret data can be extracted from a covered image but also the cover image can be completely rebuilt after the extraction process. Therefore, it is the choice in cases of secret data hiding where the full recovery of the cover image is essential. In this paper, we propose a reversible data hiding technique based on Neighbor Mean Interpolation (NMI) method utilizing the R−weighted Coding Method (RCM). Experimental results show the practicability and superiority of the proposed method over its classical counterparts, providing high performance in terms of PSNR and data hiding capacity.

Several techniques of data embedding and data hiding have been proposed and developed especially during the last two decades due to continually increasing needs of secure communication. Still image, audio and video files are the most promising digital mediums for steganography applications. However, video files have a vast potential for embedding secret data compared to other alternatives in terms of storage size. Selecting the most appropriate pixels is of great importance in the procedure of embedding secret data into video files. Unsuccessful pixel selection can trigger some negative spatial and/or temporal awareness, which eventually causes an ineffective data embedding process. In this paper, we have proposed and developed an effective blind steganography method, which uses an appropriate pixel selection mechanism, based on histogram techniques. The method we have proposed proves its success by means of perceptibility of the secret data in both spatial and temporal domains.

This paper presents a new partial optimization approach for the least significant bit (LSB) data hiding technique that can be used for protecting any secret information or data. A deterioration effect of as little as possible in an image is intended using the LSB data hiding technique and this is well realized utilizing the proposed partial optimization approach achieving the same data embedding bit rates. In the proposed approach, all of the image pixels are classified into 8 regions and then the 8 distinct ordering codings are applied to each region by the developed partial optimization encoder. Thus, the most effective outcome that has been obtained from the 8 regions means that the number of the altered bits is kept minimized. Hence, the minimal values that have been attained from the 8 regions enable decoding that ensures relatively small distortions on the extracted cover image.

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