Multimodal Claim Validation for Deepfake and Fallacy Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information validation systems struggle to accurately and efficiently identify deepfake information in multimodal environments due to the inability to dynamically recognize cross-connections and contextual clues, leading to inaccurate validation and significant resource expenditure.
Innovation Solution
A computer-implemented method that generates embeddings from multimodal experience inputs, identifies truth claims and deceptive designs, and evaluates them for logical fallacies using fine-tuned models to generate alerts and trust scores, reducing manual effort and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based engines are used for information validation, then the validation process is simple to implement, but the system fails to dynamically identify cross-connections and contextual clues, resulting in inaccurate validation
Solution Approach 1:
The patent replaces rule-based mechanical validation systems with AI/ML-based automated validation systems. The AI model dynamically analyzes multimodal information (text, images, videos) to identify truth claims, cross-connections, and contextual clues, achieving both high accuracy and automated operation without manual intervention
2Reliability
If manual validation effort is used to correct inaccurate validation, then validation accuracy can be improved, but significant time and human resources are consumed
Solution Approach 1:
The system performs self-validation through automated AI models that independently analyze multimodal information, identify truth claims, detect logical fallacies, and generate validation results without requiring human intervention. This self-service capability maintains high accuracy while eliminating time consumption associated with manual review
3Adaptability or versatility
If existing validation systems are used, then the basic validation function is provided, but deepfake information generated using AI models cannot be detected
Solution Approach 1:
The patent changes the validation parameters from static rules to dynamic AI-based analysis parameters. The system processes multiple modalities (text, images, videos) simultaneously, analyzes cross-connections between them, and detects subtle patterns and contextual clues that reveal deepfake information, achieving high detection accuracy across different content types
Data Source
AI summary
Methods and systems for validating multimodal experience inputs are disclosed. The multimodal experience inputs are received from a user and embeddings are generated based upon the multimodal experience inputs. Each of the embeddings is processed using a claim identifier model to identify at least one truth claim. The at least one truth claim is evaluated further for at least one logical fallacy from a first set of logical fallacies and a second set of logical fallacies. Based upon the evaluated at least one logical fallacy for the at least one truth claim, an alert is generated. The alert provides insights describing claim logic and veracity to warn the user about a manipulation attempt.