Digital Asset Tracking via Statistical Steganography in Images and Video
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Solution Overview
Problem
Existing digital steganography methods fail to effectively track and manage digital assets embedded in third-party games or programs, as these assets become out of control once embedded in independent video streams, leading to potential misuse and loss of revenue.
Innovation Solution
A system and method that assign unique identification codes to digital assets, breaking them down into graphical representation elements that are embedded within the assets. These elements are then concealed in multiple embodiments of the digital asset, allowing for automated decoding and identification using a computer program based on a statistically ergodic recovery mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If graphical barcodes are added to digital assets for identification, then machine identification capability is improved, but aesthetic appearance deteriorates and the solution becomes easily thwarted
Solution Approach 1:
The patent applies local quality by embedding identification data only in specific local regions of the digital asset (such as corners or borders) rather than throughout the entire asset. This allows the asset to maintain its overall aesthetic appearance while still providing machine-readable identification in specific locations.
Solution Approach 2:
The patent uses an intermediary encoding scheme where identification data is hidden within legitimate graphical elements of the asset (such as textures, patterns, or design features) rather than adding separate barcodes. This intermediary approach allows the same visual elements to serve both aesthetic and identification functions.
2Difficulty of detecting and measuring
If graphical barcodes are added to digital assets, then identification capability is improved, but the solution becomes vulnerable to occlusion and orientation issues
Solution Approach 1:
The patent applies dynamics by creating identification schemes that are adaptive to different orientations and transformations. The embedded identification data is designed to remain detectable regardless of the asset's rotation, scaling, or other transformations, making the system dynamic rather than static.
Solution Approach 2:
The patent achieves universality by designing identification elements that function across multiple orientations, scales, and transformation types. A single embedding scheme serves multiple functions: identification, orientation detection, and transformation tolerance, making the system universally applicable regardless of how the asset is presented.
3Measurement precision
If machine learning algorithms are used to identify digital assets in video streams, then identification accuracy is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent applies preliminary action by embedding identification data directly into the digital asset during the asset creation or integration phase. This preliminary embedding eliminates the need for complex runtime analysis, allowing for fast, simple detection algorithms that can operate in real-time video streams without significant computational overhead.
4Adaptability or versatility
If digital assets are embedded in third-party games or programs, then adaptability and reach are improved, but control and traceability of the assets deteriorate
Solution Approach 1:
The patent applies segmentation by separating the identification data from the asset's functional content. The identification portion is embedded independently within the asset, allowing it to be detected and tracked separately from the asset's use in different platforms and applications. This segmentation preserves traceability information even as the asset moves through multiple third-party environments.
Data Source
AI summary
A method automatically detects digital assets embedded into video frames or images. Each digital asset includes a plurality of embedded graphical representation elements with each element embedding an individual character of the digital asset's unique identification code. The video frames or images are automatically scanned for the presence of embedded graphical representation elements with each detected element decoded to extract its individual character. The resultant extracted characters are then statistically analyzed in an attempt to reconstruct the digital asset's unique identification code.