Image Origin Tracking via Hash Value Association
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Solution Overview
Problem
Search systems face inefficiencies in indexing and processing images due to the inability to use byte fingerprints or hash values to identify derivative images, leading to resource-intensive processing and potential duplication of image information in search results.
Innovation Solution
Implementing a method where a mathematical function, such as a hash function, is applied to image data to generate a value that is associated with derivative images, allowing metadata tagging to identify predecessor images and reduce redundant processing by linking derivative images to their original versions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex image processing techniques are used to identify similar images, then image identification accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The system pre-generates hash values for images and stores them in a database before search operations. When a derivative image is encountered, its hash is immediately compared against the pre-stored hashes to quickly identify the source image, avoiding the need for complex real-time image processing and similarity analysis.
Solution Approach 2:
The patent extracts the essential identifying feature (hash value) from the complete image data. By comparing only these extracted hash values rather than performing full image processing, the system achieves rapid identification of derivative images while maintaining accuracy.
2Productivity
If byte fingerprint or hash value comparison is used to identify images, then processing speed is improved, but the ability to identify derivative images is lost
Solution Approach 1:
The system adds a new dimension to hash comparison by incorporating metadata analysis. When hash values match or are similar, the system examines additional dimensions such as EXIF data, file format information, and other metadata to confirm derivative relationships, thereby maintaining reliability while using the fast hash comparison method.
Solution Approach 2:
The patent introduces metadata as an intermediary element that bridges the gap between simple hash comparison and full image analysis. By using metadata to supplement hash value matching, the system can reliably identify derivative images without sacrificing processing speed.
3Measurement precision
If complex image processing techniques are applied to each derivative image, then identification accuracy is improved, but resource consumption increases
Solution Approach 1:
The system merges the identification process by first comparing lightweight hash values and only applying complex image processing techniques when necessary. This combined approach uses simple operations for the majority of cases (quick hash comparison) and reserves resource-intensive operations for edge cases, significantly reducing overall resource consumption while maintaining accuracy.
Solution Approach 2:
The patent applies partial processing by using hash comparison for initial screening and only performing full image processing when the hash comparison indicates a potential match. This partial action approach processes only the necessary subset of images with complex algorithms, minimizing resource consumption while preserving identification accuracy.
4Loss of information
If derivative images are indexed and processed independently, then search completeness is improved, but redundancy increases
Solution Approach 1:
The system uses feedback from hash value comparisons to determine whether an image is a derivative. When a derivative image is identified through hash matching, the system provides feedback to avoid redundant indexing and processing, thereby reducing data redundancy while maintaining search completeness by still including the derivative in results linked to the source image.
Data Source
AI summary
Methods, systems, apparatus, and computer program products, including accessing first data that defines a first image, generate a first value by applying a mathematical function to the first data, generating a second image from the first data of the first image, the second image being a version of the first image and defined by second data that is different from the first data of the first image, associating the first value of the first image with the second image, generating a third image from the second data of the second image, the third image being a version of the second image and defined by third data that is different from the second data of the second image, and associating the first value of the first image with the third image.