A Dual Domain Robust Reversible Watermarking Algorithm for Frame Grouping Videos Using Scene Smoothness
Source abstract
Abstract
The proposed robust reversible watermarking algorithm addresses the compatibility challenges between robustness and reversibility in existing video watermarking techniques by leveraging scene smoothness for frame grouping videos. Grounded in the H.264 video coding standard, the algorithm first employs traditional robust watermark stitching technology to embed watermark information in the low-frequency coefficient domain of the U channel. Subsequently, it utilizes histogram migration techniques in the high-frequency coefficient domain of the U channel to embed auxiliary information, enabling successful watermark extraction and lossless recovery of the original video content. Experimental results demonstrate the algorithm’s strong imperceptibility, with each embedded frame in the experimental videos achieving a mean peak signal-to-noise ratio of 49.3830 dB and a mean structural similarity of 0.9996. Compared with the three comparison algorithms, the performance of the two experimental indexes is improved by 7.59% and 0.4% on average. At the same time, the proposed algorithm has strong robustness to both offline and online attacks: In the face of offline attacks, the average normalized correlation coefficient between the extracted watermark and the original watermark is 0.9989, and the average bit error rate is 0.0089. In the face of online attacks, the normalized correlation coefficient between the extracted watermark and the original watermark is 0.8840, and the mean bit error rate is 0.2269. Compared with the three comparison algorithms, the performance of the two experimental indexes is improved by 1.27% and 18.16% on average, highlighting the algorithm’s robustness. Furthermore, the algorithm exhibits low computational complexity, with the mean encoding and the mean decoding time differentials during experimental video processing being 3.934 and 2.273 s, respectively, underscoring its practical utility.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Steganography and Watermarking Techniques · Chaos-based Image/Signal Encryption · Computer Graphics and Visualization Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (47)
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