Digital Watermarking via Local Binary Pattern Synthesis
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Solution Overview
Problem
Existing digital watermarking techniques face challenges in maintaining robustness and imperceptibility while being computationally efficient, especially under attacks like rotation, cropping, and scaling, and often require complex transformations that consume significant computational resources.
Innovation Solution
The method employs a local binary pattern (LBP) synthesis process to embed and extract watermarks by dividing digital media into N*N blocks, modifying neighbor pixel values based on watermark bits, and using a forward LBP process for extraction, eliminating the need for complex transformations and reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex transformations are used for watermark embedding and extraction, then robustness against attacks is improved, but computational complexity increases
Solution Approach 1:
The digital media is divided into multiple non-overlapping blocks, and each block is further divided into a center region and a surrounding region. This segmentation allows the watermark embedding to be performed locally in the frequency domain for each block, reducing the computational complexity compared to transforming the entire image while maintaining robustness through distributed watermark placement across multiple blocks.
Solution Approach 2:
The patent applies different processing strategies to different regions within each block: the center region uses one approach while the surrounding region uses another. This local differentiation allows optimized computational approaches for each region, reducing overall complexity while maintaining robust watermark embedding that can withstand various attacks.
2Reliability
If complex transformations are used for watermark embedding, then watermark robustness is improved, but processing time increases
Solution Approach 1:
By segmenting the image into blocks and processing each block independently in the frequency domain, the patent reduces the overall processing time compared to global transformations, while the distributed nature of watermark placement across multiple blocks maintains robustness against attacks.
Solution Approach 2:
The patent performs a preliminary frequency transformation (e.g., DCT or DWT) on the entire image once before watermark embedding, then performs simpler local operations on the transformed coefficients during watermark insertion and extraction. This preliminary action reduces the computational burden and processing time for the actual watermark operations while maintaining robustness.
3Measurement precision
If watermark embedding modifies pixel values significantly, then watermark detectability is improved, but imperceptibility deteriorates
Solution Approach 1:
The patent embeds watermarks in the frequency domain with different strengths in different regions: stronger embedding in the center region and weaker embedding in the surrounding region. This local differentiation ensures sufficient detectability in critical areas while maintaining imperceptibility in other areas, balancing both requirements.
Solution Approach 2:
The patent transforms the image to the frequency domain and embeds watermarks by modifying frequency coefficients rather than directly modifying pixel values. This parameter transformation allows for more controlled and subtle modifications that maintain imperceptibility in the spatial domain while ensuring detectability through frequency analysis.
Data Source
AI summary
System(s) and method(s) for embedding and extracting a watermark from a digital media are described. The method may include embedding a set of watermark bits in a N*N block of the digital media based on a local binary pattern (LBP) synthesis process. The LBP synthesis process comprises changing a value of a neighbor of a central pixel of the N*N block to make the value of the neighbor greater than a value of the central pixel when a watermark bit corresponding to the neighbor is ‘1’ and changing the value of the neighbor of the central pixel to make the value of the neighbor lesser than the value of the central pixel when the watermark bit corresponding to the neighbor is ‘0’.