Reversible Fragile Watermarking Scheme for Relational Database Based on Prediction-Error Expansion

The protection of database systems content using digital watermarking is nowadays an emerging research direction in information security. In the literature, many solutions have been proposed either for copyright protection and ownership proofing or integrity checking and tamper localization. Nevertheless, most of them are distortion embedding based as they introduce permanent errors into the cover data during the encoding process, which inevitably affect data quality and usability. Since such distortions are not tolerated in many applications, including banking, medical, and military data, reversible watermarking, primarily designed for multimedia content, has been extended to relational databases. In this article, we propose a novel prediction-error expansion based on reversible watermarking strategy, which not only detects and localizes malicious modifications but also recovers back the original data at watermark detection. The effectiveness of the proposed method is proved through rigorous theoretical analysis and detailed experiments.