Digital Watermark Block Alignment for Printed Matter Alteration Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies for detecting alterations in printed matter are inefficient, requiring extensive computation and time to match feature points, making them unsuitable for rapid and accurate detection.
Innovation Solution
A system and method that embeds digital watermark data in an original image by dividing it into blocks, storing the embedded data and positions, and using these to detect and decode patterns in scanned images, aligning blocks to identify positional deviations and extract differences, thereby detecting alterations with reduced computational workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional feature point matching methods are used to detect alterations in printed matter, then detection accuracy can be achieved, but calculation time and computational workload become excessively large
Solution Approach 1:
The patent divides the original image into multiple blocks and embeds digital watermark patterns in each block. During detection, only the watermark patterns need to be extracted and matched, rather than performing comprehensive feature point matching across the entire image. This segmentation approach maintains detection accuracy while significantly reducing computational complexity and calculation time.
Solution Approach 2:
The patent embeds digital watermark patterns in the original image before printing. These pre-embedded patterns serve as predetermined reference points that facilitate rapid alignment and comparison between the original and scanned images. By having the watermark patterns prepared in advance, the system avoids the need for time-consuming feature point detection and matching during the alteration detection phase.
2Measurement precision
If digital watermark data is embedded in divided blocks of an original image, then alteration detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent employs digital watermark patterns that serve multiple functions: they act as identification markers for block alignment, provide reference points for detecting positional deviations, and enable alteration detection. This multi-functionality reduces the need for separate detection mechanisms, thereby maintaining detection accuracy while limiting the increase in system complexity.
Solution Approach 2:
The patent uses simple binary patterns (e.g., white/black dot arrangements) as digital watermarks, which are easy to generate, embed, and detect. These simplified copied patterns replace complex feature point structures, achieving robust alteration detection through straightforward pattern matching and comparison operations.
3Reliability
If comprehensive feature point matching is performed to detect alterations, then detection thoroughness is improved, but productivity decreases
Solution Approach 1:
The patent extracts only the essential digital watermark patterns from each block for comparison purposes, rather than performing comprehensive analysis of all image features. This extraction approach maintains detection thoroughness by focusing on the predetermined watermark patterns that are specifically designed to indicate alterations, while significantly improving detection speed by avoiding unnecessary computational operations.
Data Source
AI summary
A system includes circuitry configured to embed a digital watermark data in an original image, wherein the original image is divided into a plurality of blocks each block having been embedded with a pattern corresponding to each value of the digital watermark data, store, in a memory, the original image, the digital watermark data embedded in the original image, and an embedding position of the pattern of the digital watermark data in association, detect a pattern in a scanned image of a printed matter, decode the detected pattern to acquire digital watermark data included in the scanned image, align each block between the original image and the scanned image, based on the embedding position of the pattern associated with the original image and a detection position of the pattern detected from the scanned image, and obtain a difference between the original image and the scanned image aligned with each other.