A GLCM-Feature-Based Approach for Reversible Image Transformation

Recently, a reversible image transformation (RIT) technology that transforms a secret image to a freely-selected target image is proposed. It not only can generate a stego-image that looks similar to the target image, but also can recover the secret image without any loss. It also has been proved to be very useful in image content protection and reversible data hiding in encrypted images. However, the standard deviation (SD) is selected as the only feature during the matching of the secret and target image blocks in RIT methods, the matching result is not so good and needs to be further improved since the distributions of SDs of the two images may be not very similar. Therefore, this paper proposes a Gray level co-occurrence matrix (GLCM) based approach for reversible image transformation, in which, an effective feature extraction algorithm is utilized to increase the accuracy of blocks matching for improving the visual quality of transformed image, while the auxiliary information, which is utilized to record the transformation parameters, is not increased. Thus, the visual quality of the stego-image should be improved. Experimental results also show that the root mean square of stego-image can be reduced by 4.24% compared with the previous method.