Research on blind reversible database watermarking algorithm based on dual embedding strategy

Massive databases encounter security such as data theft, illegal copying, and copyright infringement during creating, transmitting, and sharing of big data. The reversible data watermarking technology can effectively solve these problems, which can extract the watermark information accurately and recover the original carrier data without any distortion. However, most existing methods extract watermark information non-blindly and cannot effectively achieve a balance between watermark embedding capacity and data distortion. This paper proposes a blind reversible database watermarking method based on dual embedding, which combines histogram shifting and distortion-free watermarking methods to achieve an adaptive selection of histogram bins, blind extraction of watermark information, and carrier data recovery. The proposed method preprocesses the database tuples by scrambling them and constructs a prediction error histogram using first-layer tuples in square prediction within each group. The watermark information is embedded through adaptive selection and expansion of histogram bins, while the distortion-free watermarking method is used in another layer to assist in the recovery of original carrier data. The experimental results show that the proposed method can achieve an embedding capacity of more than three times the capacity of existing methods. It can also achieve blind watermark extraction and outperform some other state-of-the-art methods.