Adaptive Embedding Pattern for Grayscale-Invariance Reversible Data Hiding

Abstract: In traditional reversible data hiding (RDH) methods, researchers pay attention to enlarge the embedding capacity (EC) and to reduce the embedding distortion (ED). Recently, a completely novel RDH algorithm was developed to embed secret data into color image without changing the corresponding grayscale [1], which largely expands the applications of RDH. In [1], for color image, channel R and channel B are exploited to carry secret information, channel G is adjusted for balancing the modifications of channel R and channel B to keep the invariance of grayscale. However, we found that the embedding performance (EP) of that method is still unsatisfied and could be further enhanced. To improve the EP, an adaptive embedding pattern is introduced to enhance the competence of algorithm for selectively embedding different bits of secret data into pixels according to context information. Moreover, a novel two-level predictor is designed by uniting two normal predictors for reducing the ED for embedding more bits. Experimental results demonstrate that, compared to the previous method, our scheme could significantly enhance the image fidelity while keeping the grayscale invariant.