Watermark Outage Detection Using Markov Models
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Solution Overview
Problem
Existing watermarking techniques face challenges in reliably and consistently encoding watermarks in media signals, leading to false alarms due to intentional non-encoded portions like commercials, which can result in the 'cry wolf' effect, where actual encoding issues are overlooked.
Innovation Solution
Implementing a dynamic analysis system that models watermark presence patterns to distinguish between expected and unexpected outages by analyzing onset times and durations, using Markov models and clustering techniques to accurately trigger alarms only for unintentional watermark encoding failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simple watermark monitoring is used to detect all watermark absences, then detection coverage is improved, but false alarm rate increases due to intentional non-encoded portions like commercials
Solution Approach 1:
The system performs preliminary learning during a training period to establish expected watermark outage patterns (onset times, durations, frequencies) before actual monitoring begins. This preliminary action enables the system to distinguish between expected outages during commercials and unexpected outages indicating encoding failures, thereby reducing false alarms while maintaining detection reliability.
Solution Approach 2:
The system continuously compares detected watermark outage characteristics against the learned expected patterns and provides feedback to determine whether an outage is expected or unexpected. This feedback mechanism allows the system to suppress false alarms for expected outages while alerting on unexpected encoding failures, resolving the contradiction between comprehensive detection and false alarm reduction.
2Object-generated harmful factors
If dynamic analysis with multiple models is implemented to reduce false alarms, then false alarm rate is reduced, but system complexity increases
Solution Approach 1:
The analysis system is segmented into distinct functional components: a learning module that establishes expected patterns during training, a detection module that monitors watermark presence, and an analysis module that compares detected patterns against learned expectations. This segmentation reduces overall complexity by making each component's function explicit and manageable, while still achieving sophisticated false alarm reduction through their coordinated operation.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for watermark outage detection. Example methods include evaluating an onset time and duration of a detected watermark outage based on a model of expected watermark outages to determine whether the detected watermark outage corresponds to at least one of the expected watermark outages represented in the model. Example methods further include generating an alert in response to determining the detected watermark outage does not correspond to at least one of the expected watermark outages included in the model, and suppressing the alert in response to determining the detected watermark outage corresponds to the at least one of the expected watermark outages represented in the model.