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