A Novel Robust Reversible Watermarking Scheme for Protecting Authenticity and Integrity of Medical Images

Abstract

It is of great importance in telemedicine to protect authenticity and integrity of medical images. They are mainly addressed by two technologies, which are region of interest (ROI) lossless watermarking and reversible watermarking. However, the former causes biases on diagnosis by distorting region of none interest (RONI) and introduces security risks by segmenting image spatially for watermark embedding. The latter fails to provide reliable recovery function for the tampered areas when protecting image integrity. To address these issues, a novel robust reversible watermarking scheme is proposed in this paper. In our scheme, a reversible watermarking method is designed based on recursive dither modulation (RDM) to avoid biases on diagnosis. In addition, RDM is combined with Slantlet transform and singular value decomposition to provide a reliable solution for protecting image authenticity. Moreover, ROI and RONI are divided for watermark generation to design an effective recovery function under limited embedding capacity. Finally, watermarks are embedded into whole medical images to avoid the risks caused by segmenting image spatially. Experimental results demonstrate that our proposed lossless scheme not only has remarkable imperceptibility and sufficient robustness but also provides reliable authentication, tamper detection, localization, and recovery functions, which outperforms existing schemes for protecting medical images. INDEX TERMS Robust reversible watermarking, authenticity, integrity, medical image.

Robust Watermarking Algorithm for Medical Volume Data in Internet of Medical Things

“…Especially when the scaling factor is as low as 0.1, its watermark can still be fully extracted with the NC value 1.0, which performs much better than other algorithms. In addition, in the comparison results of the downward translation, as shown in figure 31(g), the NC value of the proposed algorithm is slightly higher than the algorithm proposed by Liu et al [13] and Loan et al [18] When the downward translation exceeds 13%, the algorithm proposed in literature [13] and [18] are slightly higher than our algorithm after moving downwards by 15% and 20%, respectively. From the analysis of the introduction, the algorithm proposed by Loan et al [18] embedded the watermark by pre-selecting and setting the difference of the IF coefficient and its range, which effectively managed the IF coefficient of the image and enabled it to perform well in the downward translation.…”

“…To further verify the robustness of the proposed algorithm, the standard brain volume data provided by matlab R2019b was used for a simulation comparison test. We compare the proposed algorithm with the anti-salt-and-noise-resistant brain magnetic resonance image watermarking method proposed by Mousavi et al [8], the robust watermarking scheme using coefficient difference and chaotic encryption proposed by Loan et al [18], and the reversible watermarking scheme based on recursive jitter modulation (RDM) proposed by Liu et al [13] Some data results are shown in table 11, and their detailed comparison performance are shown in Fig. 31.…”

“…From the analysis of the introduction, the algorithm proposed by Loan et al [18] embedded the watermark by pre-selecting and setting the difference of the IF coefficient and its range, which effectively managed the IF coefficient of the image and enabled it to perform well in the downward translation. The algorithm proposed by Liu et al [13] generated watermarks for ROI and RONI respectively, and designed a recovery function for ROI, which ensured that when the percentage of downward vertical translation became larger, the watermark can still be efficiently extracted. In general however, the descent curve of the proposed algorithm is stable without obvious fluctuations, and when the downward vertical translation reached 25%, the NC value is slightly higher than the algorithm proposed by Liu et al [13] and Loan et al [18], and significantly higher than the algorithm proposed by Mousavi et al [8].…”

“…The algorithm proposed by Liu et al [13] generated watermarks for ROI and RONI respectively, and designed a recovery function for ROI, which ensured that when the percentage of downward vertical translation became larger, the watermark can still be efficiently extracted. In general however, the descent curve of the proposed algorithm is stable without obvious fluctuations, and when the downward vertical translation reached 25%, the NC value is slightly higher than the algorithm proposed by Liu et al [13] and Loan et al [18], and significantly higher than the algorithm proposed by Mousavi et al [8]. In the downward translation attacks, the overall performance of our proposed algorithm is better than that of the existing algorithms.…”

“…It encoded the watermark data to the region of interest, and the imperceptibility is poor. Liu et al [13] designed a reversible medical watermarking method based on recursive jitter modulation (RDM), which combined RDM with Slantlet transform and singular value decomposition to generate ROI and RONI watermarking respectively, reducing the risk of spatial segmentation and avoiding diagnostic errors by lossless ROI recovery. However, it is not robust to geometric attacks such as rotation.…”