Efficient Watermark Embedding and Extracting in Raw Digital Video: Leveraging the Least Significant Bit Technique in the Spatial Domain
Keywords:
Watermark, Digital Video Watermarking, Least Significant Bit (LSB), Least Significant Nibble (LSN), Uncompressed Video, Spatial DomainAbstract
With the emergence of the internet, digital media creators have been able to distribute their works by making them available on web pages or public chatting forums. The person with permission to access these pages or forums can copy these media, modify them, and get an identical original copy. Using digital content has shown various issues, like how authors can ensure the copyright of their works. Additional information has been included with multimedia to protect the media before distribution. This research proposed a new method for embedding watermarks in colored videos in the spatial domain. The embedding and extracting process of the watermark is based on the principle of probability. The embedding process occurs in the Least Significant bit (LSB) of the pixel, determined by the probability of whether the number of “1” in the least Significant Nibble (LSN) of the pixel is even or odd. If it's even, and the watermark bit is “1”, the second bit of the pixel will be complemented; otherwise, the pixel remains unchanged. If it's odd, and the watermark bit is 0, the complement will be done on the second bit; otherwise, there is no change. To preserve imperceptibility and quality, this process applies to all video frames, specifically in the blue channel. After undergoing proposed attacks, watermark extraction from the video is also based on the probability principle. It involves collecting even LSNs and comparing them with the total number of pixels in one block. If the sum exceeds the total count of pixels in the block, the extracted bit is 0; otherwise, it's 1. The effectiveness of the proposed algorithm is demonstrated by obtaining optimal values for quality metrics like PSNR, SSIM, and MSE. Regarding robustness, the Normalized Cross-Correlation (NCC) metric is used, yielding highly favorable results that showcase the algorithm's strength and ability to extract watermarks after various attacks, such as distortion and geometric attacks like cropping and resizing. This system is perfect in terms of Imperceptibility, Data Payload, Computational Complexity, Computational Time and Error Probability.
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