Reversible watermarking enables the embedding of useful information in a host signal without any loss of host information. Tian's difference-expansion technique is a high-capacity, reversible method for data embedding. However, the method suffers from undesirable distortion at low embedding capacities and lack of capacity control due to the need for embedding a location map. We propose a histogram shifting technique as an alternative to embedding the location map. The proposed technique improves the distortion performance at low embedding capacities and mitigates the capacity control problem. We also propose a reversible data-embedding technique called prediction-error expansion. This new technique better exploits the correlation inherent in the neighborhood of a pixel than the difference-expansion scheme. Prediction-error expansion and histogram shifting combine to form an effective method for data embedding. The experimental results for many standard test images show that prediction-error expansion doubles the maximum embedding capacity when compared to difference expansion. There is also a significant improvement in the quality of the watermarked image, especially at moderate embedding capacities.

Kevcrsihle watcnnarking has hecome a highly desirahlc suhset of fragile watermarking ror sensitive digital imagery in application domains such as military and medical because of the ability to emhed data with zero loss of host information. This reversibility enablcs the recovery of the original host contcnt upon verification of the authenticity of the received contcnt. We propose a new rcversihle watermarking algorithm. The algorithm exploits the corrclation inherent among the neighboring pixels in an image region using a predictor. The prediction-error at each location is calculated and, depending on the amounl of information to he embedded, locations are selected for embedding. Data embedding is done by expanding the prcdiction-error values. A compressed location map of the embedded locations is also embedded along with the information hits. Our algorithm exploits the redundancy in the image to achieve very high data embedding rates while keeping the resulting distortion low.

We pnipme a new reiwrsible (lossless) nutemlarking dgorithnifor digital Oitages. Being reversible, tlle algorirlm enobles (lie recover) qfilie original liosl ii@nlatian tipon the extraction cf the embedded infiinilaiiun.The /imposed techriiqne e.rp1oit.s the inltrrent correlarion on~orig the adjacent pi.els in ai1 image region using a predict<>,: Tlie infiimuirion hits ore ernbedded info the prediction errors, wlticlr enables 11s to einhed a large payload wltile keeping the di.stonion Ion: A Irishigram sliifr ai the encuder enables the decoder io identi& the eriiberlded location.