Clustering Highly-Similar Images for Original Detection
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Solution Overview
Problem
Current methods for identifying tampered images, especially those that are highly similar, are not consistently accurate and struggle with skilled forgeries, leading to inefficiencies in image evaluation processes.
Innovation Solution
A computer-implemented process using a comparison engine to cluster highly-similar images based on comparison scores, with a machine learning engine selecting one cluster as representing the original image, and a graphical user interface for visual display and user interaction to tag or delete non-original images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep fake and forgery detection algorithms are employed, then image authentication is attempted, but accuracy is not consistently high in differentiating between highly-similar datasets
Solution Approach 1:
The patent segments the image authentication problem into multiple components: (1) generating multiple modified versions of the original image using different transformation techniques, (2) training separate detection models on each transformed dataset, and (3) aggregating results from multiple models. This segmentation allows each component to specialize in detecting specific types of manipulations, thereby improving overall accuracy and consistency in identifying highly-similar tampered images.
2Productivity
If thousands of employees are employed for monitoring, then image monitoring coverage is increased, but operational complexity and cost increase
Solution Approach 1:
The patent implements automated self-service mechanisms where the system independently performs image authentication without human intervention. The detection models automatically analyze images, identify tampering, and generate results. This automation eliminates the need for large teams of manual reviewers while maintaining high monitoring coverage, thereby reducing operational complexity and costs while preserving productivity.
3Ease of operation
If brute force method with thousands of employees is used, then image monitoring is performed, but efficiency decreases
Solution Approach 1:
The patent replaces the mechanical system of manual image review by thousands of employees with an automated computational system. Machine learning models and algorithms process images electronically, substituting human manual evaluation with automated digital processing. This substitution dramatically improves efficiency while maintaining ease of operation, as the automated system can handle vast numbers of images quickly and consistently without the bottlenecks of manual processing.
Data Source
AI summary
A computer hardware system includes a processor including a comparison engine and configured to perform the following executable operations. Using the comparison engine, each image in a dataset of highly-similar images is compared to every other image in the dataset of highly-similar images to generate a comparison score for each image-image pair. The images in the dataset of highly-similar images are clustered into a plurality of image clusters based upon the comparison scores. One of the plurality of image clusters is selected as representing an original image. A data processing operation is performed on the dataset of highly-similar images based upon the selection of the one of the plurality of image clusters as representing the original image.