TY - JOUR
T1 - A Highly Robust Reversible Watermarking Scheme Using Embedding Optimization and Rounded Error Compensation
AU - Tang, Yichao
AU - Wang, Shuai
AU - Wang, Chuntao
AU - Xiang, Shijun
AU - Cheung, Yiu-Ming
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Contract 62172165 and Contract 62272197, in part by the Guangdong Major Project of Basic and Applied Basic Research under Grant 2019B030302008, in part by the Natural Science Foundation of Guangdong Province under Grant 2022A1515010325, in part by the Science and Technology Program of Guangzhou under Grant 201902010081, in part by the Guangzhou Basic and Applied Basic Research Project under Contract 202201010742, in part by the NSFC/Research Grants Council (RGC) Joint Research Scheme under Grant N_HKBU214/21, in part by the General Research Fund of RGC under Grant 12201321, and in part by the Hong Kong Baptist University (HKBU) under Grant RC-FNRA-IG/18-19/SCI/03.
PY - 2023/4
Y1 - 2023/4
N2 - The robust reversible watermarking (RRW) requires high robustness and capacity on the condition of reversibility and imperceptibility, which still remains a big challenge nowadays. In this paper, we propose a two-stage RRW scheme that improves robustness and capacity through embedding optimization and rounded error compensation. The first stage inserts a robust watermark into the selected Pseudo-Zernike moments (PZMs) by using an adaptive normalization method and an optimized embedding strategy. Specifically, the adaptive normalization method achieves both an invariance to pixel amplitude variation and a balance between robustness and imperceptibility, and the optimized embedding strategy reduces embedding distortions remarkably. The watermarked PZMs are inversely transformed to generate the robustly watermarked image, in which rounded errors caused in the inverse transformation is compensated elaborately and thus a larger capacity can be obtained at the same embedding distortion. The second stage embeds a reversible watermark consisting of errors between the robust watermark embedded image and the original one, aiming at achieving the reversibility in case of no attacks. Extensive experimental simulations show that the proposed scheme provides strong robustness against common signal processing, including AWGN, salt-and-pepper noise, JPEG, JPEG2000, median filtering, mean filtering, geometrical transformations involving rotation and scaling, and a compressive sensing attack exemplified by two-dimensional compressive sensing, which outperforms the state-of-the-art schemes. Our code is available at https://github.com/yichao-tang/PZMs-RRW.
AB - The robust reversible watermarking (RRW) requires high robustness and capacity on the condition of reversibility and imperceptibility, which still remains a big challenge nowadays. In this paper, we propose a two-stage RRW scheme that improves robustness and capacity through embedding optimization and rounded error compensation. The first stage inserts a robust watermark into the selected Pseudo-Zernike moments (PZMs) by using an adaptive normalization method and an optimized embedding strategy. Specifically, the adaptive normalization method achieves both an invariance to pixel amplitude variation and a balance between robustness and imperceptibility, and the optimized embedding strategy reduces embedding distortions remarkably. The watermarked PZMs are inversely transformed to generate the robustly watermarked image, in which rounded errors caused in the inverse transformation is compensated elaborately and thus a larger capacity can be obtained at the same embedding distortion. The second stage embeds a reversible watermark consisting of errors between the robust watermark embedded image and the original one, aiming at achieving the reversibility in case of no attacks. Extensive experimental simulations show that the proposed scheme provides strong robustness against common signal processing, including AWGN, salt-and-pepper noise, JPEG, JPEG2000, median filtering, mean filtering, geometrical transformations involving rotation and scaling, and a compressive sensing attack exemplified by two-dimensional compressive sensing, which outperforms the state-of-the-art schemes. Our code is available at https://github.com/yichao-tang/PZMs-RRW.
KW - Pseudo-Zernike moments
KW - Robust reversible watermarking
KW - compensation information
KW - geometric deformations
UR - http://www.scopus.com/inward/record.url?scp=85141446981&partnerID=8YFLogxK
U2 - 10.1109/TCSVT.2022.3216849
DO - 10.1109/TCSVT.2022.3216849
M3 - Journal article
SN - 1051-8215
VL - 33
SP - 1593
EP - 1609
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
IS - 4
ER -