Content Receiving Device Identification Through Anti-Collusion Watermarks
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Solution Overview
Problem
Existing systems face challenges in effectively identifying recipients of multimedia content when collusion attacks modify the content, requiring a large number of anti-collusion codes to determine the recipients accurately.
Innovation Solution
The system employs digital watermarks and anti-collusion codes that are imperceptible to humans but detectable by computers, using machine-learning techniques to embed unique identifiers in multimedia content, and calculates probabilities of user involvement in collusion attacks based on anti-collusion codes, even in the presence of modified content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital watermarks and anti-collusion codes are embedded in multimedia content to identify recipients, then the ability to trace pirated content improves, but the computational resources and complexity required to detect and analyze these codes increases
Solution Approach 1:
The system embeds anti-collusion codes and digital watermarks into multimedia content during the content creation or distribution phase, before any potential piracy occurs. This preliminary embedding ensures that when pirated content is detected later, the identification infrastructure is already in place, eliminating the need for complex real-time analysis systems.
Solution Approach 2:
The patent uses digital watermarks that create imperceptible copies of identification data within the multimedia content itself. These watermark copies are embedded throughout the content and can be extracted using simple correlation algorithms, avoiding the need for complex forensic analysis systems.
2Reliability
If a large number of anti-collusion codes are used to determine recipients accurately when content is modified via collusion attacks, then the reliability of recipient identification improves, but the computational resources required to process and analyze the codes increases
Solution Approach 1:
The system employs anti-collusion codes with properties that provide sufficient redundancy to withstand collusion attacks. Rather than using excessive numbers of codes, the patent designs codes with specific mathematical properties (such as those based on error-correcting codes or spread spectrum techniques) that achieve reliable identification with optimal computational efficiency.
Solution Approach 2:
The patent transforms the anti-collusion codes into different parameter representations that are more efficient for detection. By changing the mathematical representation or encoding format of the codes, the system maintains high reliability in identifying recipients even when content is modified, while reducing the computational energy required for analysis.
3Shape
If digital watermarks are made imperceptible to humans to maintain content quality, then the visual quality of multimedia content improves, but the difficulty of detecting and extracting watermarks by computers increases
Solution Approach 1:
The patent applies digital watermarks with locally optimized properties throughout the multimedia content. Different regions or segments of the content may contain watermarks with different characteristics tailored to local features, ensuring that watermarks remain imperceptible to human viewers while maintaining detectability by computer algorithms designed to recognize specific local patterns.
Solution Approach 2:
The system uses composite watermarking techniques that combine multiple watermarking methods or layers within the same content. This composite approach creates watermarks that are statistically imperceptible to human perception (maintaining content quality) while providing multiple detectable signals for computer-based detection systems, effectively decoupling human perception from machine detection.
Data Source
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AI summary
A method for identifying from among network-connected devices a particular device likely associated with a theft of distributed content includes obtaining content, the content having been distributed from a particular one of the network-connected devices, identifying in the obtained content anti-collusion codes, and determining the particular one of the network-connected devices is likely associated with the theft of distributed content when an aggregated probability calculated using the identified anti-collusion codes is equal to or exceeds a predetermined threshold.