Augmented Reality Fraud Detection Scam Score Calculation
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Solution Overview
Problem
It is challenging to distinguish fraudulent content in advertisements, particularly in digital formats, as existing technologies lack effective methods to assess the likelihood of scam content automatically and accurately.
Innovation Solution
An augmented reality (AR) electronic computing device method that extracts a dataset from digital advertisements, identifies actionable content, requests identification and feedback information, and calculates a scam score using coefficients weighted by consumer and professional feedback to determine the likelihood of fraudulent content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated scam detection methods are implemented, then the ability to identify fraudulent content is improved, but the complexity of the detection system increases
Solution Approach 1:
The scam detection system is divided into multiple independent modules: image processing module for extracting advertisement content, data extraction module for obtaining advertiser information, feedback analysis module for collecting consumer reviews, and scoring module for calculating scam scores. Each module performs a specific function, making the overall complex system manageable and maintainable while achieving high detection accuracy through coordinated operation of specialized components.
2Reliability
If multiple data sources and feedback information are analyzed, then the reliability of scam score calculation is improved, but the time required for detection increases
Solution Approach 1:
The system pre-establishes a database of advertiser information, consumer feedback, and scam patterns before actual detection occurs. When analyzing an advertisement, the system quickly retrieves pre-collected data rather than gathering it in real-time. Feedback information from multiple consumers is pre-aggregated and weighted, allowing the scoring module to calculate reliable scam scores rapidly by combining pre-processed data from multiple sources without excessive time delays.
3Measurement precision
If comprehensive feedback information from multiple categories is collected, then the precision of fraudulent content identification is improved, but the quantity of data to be processed increases
Solution Approach 1:
The system extracts only the most relevant and discriminating features from the comprehensive feedback data. Instead of processing all raw feedback information, the system identifies and extracts key indicators such as negative sentiment scores, complaint frequencies, and verified scam markers from consumer reviews. This extraction process filters out redundant data while retaining the essential information needed for precise fraud identification, reducing data volume without sacrificing detection precision.
Data Source
AI summary
An augmented reality (AR) electronic computing device for determining a likelihood of fraudulent content in an advertisement includes receiving a digital image of the advertisement. A dataset of information relating to the advertisement is extracted from the digital image. Content related to the advertisement is identified from the dataset of information. The identified content is used to request identification information regarding the advertisement. The identification information and supplemental information are used to calculate a scam score for the advertisement. The scam score indicates the likelihood of fraudulent content in the advertisement.