Handling Uneven Embedding Capacity in Binary Images: A Revisit

Hiding data in binary images can facilitate the authentication and annotation of important document images in digital domain. A representative approach is to first identify pixels whose binary color can be flipped without introducing noticeable artifacts, and then embed one bit in each non-overlapping block by adjusting the flippable pixel values to obtain the desired block parity. The distribution of these flippable pixels is highly uneven across the image, which is handled by random shuffling in the literature. In this paper, we revisit the problem of data embedding for binary images and investigate the incorporation of a most recent steganography framework known as the wet paper coding to improve the embedding capacity. The wet paper codes naturally handle the uneven embedding capacity through randomized projections. In contrast to the previous approach, where only a small portion of the flippable pixels are actually utilized in the embedding, the wet paper codes allow for a high utilization of pixels that have high flippability score for embedding, thus giving a significantly improved embedding capacity than the previous approach. The performance of the proposed technique is demonstrated on several representative images. We also analyze the perceptual impact and capacity-robustness relation of the new approach