Invisible Video Watermark Method Based on Maximum Voting and Probabilistic Superposition

Invisible watermarking is an essential measure for media publishers to declare ownership of their content, in the case of minimizing the impact on the viewing experience. In dealing with active attacks such as noise attacks, filtering attacks, geometric attacks, and lossy compression attacks, existing research still has great limitations. In this paper, through probabilistically superposition of "perturbed watermark obtain by maximum voting method" and "determined Bernoulli distribution" to restore the real embedded watermark, is used to deal with complex attack situations. Specifically, the special embedded watermark is obtained by sampling from the n-fold Bernoulli distribution with parameter p. Secondly, the HAAR wavelet transform is performed on the YUV channel of the video fixed-interval image to extract its low-pass component. Then Discrete Cosine Transform is performed to obtain its frequency domain representation. The watermark information is embedded into the frequency domain representation's singular to realize the embedded invisible watermark of video. The watermark bit of every block is determined by the maximum voting method for the disturbed watermark that performs DCT and SVD operations on the low-pass component of YUV channels. At this time, the determined Bernoulli distribution is probabilistically superimposed on the watermark information to guarantee distribution consistency. Finally, mean value operation and cluster processing are performed on the watermark information to reduce volatility. Experiments show that the method proposed in this paper has apparent advantages in solving Invisible video watermarking. With a PSNR of 41 and a corresponding BAR of 0.86, our team achieves 3rd place on the leaderboard of the Invisible Video Watermark Challenge 2023.