Robust lossless data hiding using clustering and statistical quantity histogram

Lossless data hiding methods usually fail to recover the hidden messages completely when the watermarked images are attacked. Therefore, the robust lossless data hiding (RLDH), or the robust reversible watermarking technique, is urgently needed to effectively improve the recovery performance. To date a couple of methods have been developed; however, they have such drawbacks as poor visual quality and low capacity. To solve this problem, we develop a novel statistical quantity histogram shifting and clustering-based RLDH method or SQH-SC for short. The benefits of SQH-SC in comparison with existing typical methods include: (1) strong robustness against lossy compression and random noise due to the usage of k-means clustering; (2) good imperceptibility and reasonable performance tradeoff due to the consideration of the just noticeable distortion of images; (3) high capacity due to the flexible adjustment of the threshold; and (4) wide adaptability and good stability to different kinds of images. Extensive experimental studies based on natural images, medical images, and synthetic aperture radar (SAR) images demonstrate the effectiveness of the proposed SQH-SC.