Watermark Detection Payload Alignment
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Solution Overview
Problem
Existing watermark detection systems face challenges in reliably detecting payloads when changes occur within the detection window, leading to reduced reliability or failure in detection due to noise and false payloads, and current methods to mitigate these issues increase complexity or limit payload update frequency.
Innovation Solution
The method involves selecting a point in the content stream where the first segment ends and the second segment begins, sampling the stream into two sets, and applying a transformation to the second set based on the relationship between the payloads in both segments to align features, allowing for robust detection even with payload changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the detector accumulates a large number of samples to improve detection reliability, then detection reliability is improved, but the detection window size increases which makes it more likely to include payload changes that act as noise and hamper detection
Solution Approach 1:
The detection window is divided into multiple sub-windows, each corresponding to a specific payload value. The detector processes samples in these segmented windows separately, allowing it to maintain large accumulation sizes for reliability while avoiding the degradation caused by payload changes within a single undivided window. Each sub-window is associated with a specific payload hypothesis, enabling the system to handle payload variations systematically.
Solution Approach 2:
The detector dynamically adjusts its processing by evaluating multiple payload hypotheses across different sub-windows. Instead of using a fixed detection approach, the system adapts by testing different payload values in different sub-windows and combining results, allowing it to maintain high detection reliability even when payloads change within the overall detection window.
2Device complexity
If the detector uses a fixed detection window to simplify processing, then device complexity is reduced, but detection reliability decreases when payload changes occur within the window
Solution Approach 1:
The fixed detection window is segmented into multiple sub-windows, each associated with a specific payload hypothesis. This segmentation allows the detector to maintain a fixed overall window structure (preserving simplicity) while internally handling payload changes through the segmented approach. Each sub-window processes samples assuming a specific payload value, and the results are combined to achieve reliable detection even when payloads change.
Solution Approach 2:
The detector changes the parameter being tested by evaluating multiple payload hypotheses across different sub-windows. Instead of fixing the payload assumption, the system varies the payload parameter across sub-windows and combines the results, allowing it to maintain fixed window simplicity while adapting to payload changes through parameter variation.
3Reliability
If the system uses less frequent payload changes to avoid detection failures, then detection reliability is improved, but the granularity of the timestamp in the payload is limited
Solution Approach 1:
The detection process is segmented into multiple sub-windows, each handling a specific payload hypothesis. This allows the system to support more frequent payload updates because the segmented structure can accommodate payload changes by assigning different sub-windows to different payload values. The overall detection reliability is maintained through the combination of results from all sub-windows.
Solution Approach 2:
The system dynamically adapts to payload changes by evaluating multiple hypotheses across sub-windows. This dynamic approach allows frequent payload updates because the detector can adjust its processing to account for different payload values in different sub-windows, maintaining reliability while supporting higher update frequencies than traditional fixed-window approaches.
4Reliability
If the detector tries multiple detection windows of different sizes to handle payload changes, then detection reliability is improved, but detector complexity increases
Solution Approach 1:
Instead of trying multiple detection windows of different sizes, the invention segments a single detection window into multiple sub-windows. Each sub-window is associated with a specific payload hypothesis and processes samples accordingly. This segmentation approach achieves the reliability benefits of handling different payload scenarios without the complexity of managing multiple separate detection windows of different sizes.
Solution Approach 2:
The segmented detection window structure serves multiple functions simultaneously: it handles different payload hypotheses, maintains appropriate accumulation sizes for reliability, and provides a systematic approach to handling payload changes. This multi-functional design eliminates the need for multiple separate detection windows, reducing overall detector complexity while maintaining high reliability.
Data Source
AI summary
A method of detecting a payload embedded using watermarking in a content stream, the payload being different in a first and a second segment of the content stream, a payload in the second segment having a predetermined relationship with a payload in the first segment, is described. The method selects a point in the content stream where the first segment is likely to end and the second segment to begin, samples the stream to obtain a first set of samples that is before the chosen point and a second set of samples that is after the chosen point, and detects the payload on a combination of the first set and a transformation of the second set, where the transformation is based on the assumption that the second set is from the second segment and exploits the relationship that exists between the payloads in the first and second segments.