Digital Content Authenticity Screening Before Publication
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Solution Overview
Problem
Existing methods for identifying fraudulent digital content, particularly those generated by conversational AI, are ineffective in detecting complex schemes and often fail to catch such content before publication, leading to high percentages of fraudulent information remaining available for viewing.
Innovation Solution
A machine learning method that analyzes digital content by generating fraudulent digital content data markers through relationships with known peer data points, using a trained machine learning assessment model to identify patterns and similarities, and applying distance operators to detect fraudulent content before publication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If post-publication review is used to identify fraudulent digital content, then the review process can be conducted with existing content, but fraudulent content remains available for viewing and relying even for a short period, and high percentages of fraudulent information remain undetected
Solution Approach 1:
The patent applies preliminary action by conducting authenticity assessment of digital content before it is published. The system analyzes content using machine learning models and comparison with known fraudulent content patterns prior to publication, preventing fraudulent content from being made available in the first place, thus eliminating the time loss associated with post-publication detection
2Loss of time
If semantic analysis is used for pre-publication detection, then fraudulent content can be identified before publication, but the system only detects very simple manipulation schemes and fails to detect complex fraudulent content generation
Solution Approach 1:
The patent applies parameter changes by transforming the detection approach from simple semantic analysis to a multi-parameter machine learning assessment. The system uses multiple features including text similarity, metadata analysis, source verification, and pattern recognition against known fraudulent content, significantly improving detection accuracy for complex fraudulent content while maintaining pre-publication detection capability
Solution Approach 2:
The patent applies composite materials by combining multiple detection methods and data sources into a composite assessment system. The machine learning model integrates various analysis techniques including semantic analysis, pattern recognition, metadata examination, and comparison with known fraudulent content databases, creating a robust multi-component detection system that handles both simple and complex fraudulent content
Data Source
AI summary
Disclosed is a digital content pre-publication assessment approach using online and centralized features to train a machine learning assessment model implemented to assess the validity of certain digital content prior to its publication. The machine learning assessment model generating pre-publication identification of fraudulent digital content based on a relational technology that identifies, compares, and assesses connections between digital content elements (such as time patterns, sentence structure, proximity, characterization similarities, etc.) to build an intelligent assessment tool that identifies singularities and patterns within sets of digital content elements to identify fraudulent sources of digital content. The model creating an assessment of the authenticity of digital content by screening digital content against peer data records, the screening multiplied according to a plurality of iteration steps. Singularities corresponding to linkages may be weighted according to various known fraudulent characteristics. Digital content may comprise reviews generated on consumer sites and/or publicly accessible commentary sites.