Contrastive Loss Neural Network for Fraudulent Profile Detection
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Solution Overview
Problem
Current methods for detecting fraudulent online profiles are inadequate, as they often require significant human resources and are not accurate enough, especially for image analysis, leading to potential security risks and loss of user trust in social media platforms.
Innovation Solution
An automated technique using a contrastive loss function, trained on a computer system, compares new profile information, including images and identifiers, to existing profiles to determine potential fraudulent activities, employing neural networks to generate metrics that indicate the likelihood of a new profile being fake, thereby reducing the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated techniques are used for profile analysis, then productivity increases, but measurement precision deteriorates
Solution Approach 1:
The patent introduces trained neural networks as intermediary components between the input profile images and the fraud detection decision. These neural networks serve as specialized mediators that process image data and generate fraud indicators, enabling automated analysis while maintaining high precision through learned patterns from training data.
Solution Approach 2:
The system transforms image data into different parameter representations through neural network processing. The images are converted into feature vectors and fraud indicators through mathematical transformations, allowing the system to analyze visual information in a format suitable for automated decision-making while preserving discriminative power.
2Measurement precision
If human resources are used for profile verification, then measurement precision improves, but productivity deteriorates
Solution Approach 1:
The system implements self-service through automated neural network-based analysis that processes profile images without human intervention. The trained models independently evaluate new profiles, generate fraud indicators, and make detection decisions, enabling the system to serve itself in the fraud detection task while maintaining consistency and scalability.
Solution Approach 2:
The patent replaces the mechanical human review process with an automated computational system. Neural networks substitute for human analysts by performing image analysis and fraud detection through algorithmic processing, eliminating the need for manual verification while maintaining detection capabilities through learned patterns.
3Reliability
If traditional detection methods are used, then device complexity remains low, but reliability deteriorates
Solution Approach 1:
The patent segments the fraud detection system into distinct functional modules: image processing components, neural network analysis layers, metric generation units, and decision-making components. This segmentation allows each module to specialize in specific tasks, improving overall reliability through modular design while managing complexity through clear separation of concerns.
Solution Approach 2:
The neural network components serve multiple functions within the system: they process images, extract features, generate fraud indicators, and support detection decisions. This multi-functionality reduces the need for separate specialized components, maintaining reliability through versatile tools while managing system complexity.
Data Source
AI summary
Techniques are disclosed relating to methods that include training, by a new profile process executing on a computer system, a contrastive loss function to identify fraudulent images associated with a particular entity. The new profile process may receive new profile information that includes a new profile image and a new profile identifier and compare the new profile identifier to one or more existing profile identifiers. In response to determining that one or more existing profile identifiers satisfy a threshold identifier metric, a particular neural network, using the contrastive loss function, may compare the new profile image to one or more existing profile images corresponding to the one or more existing profile identifiers. The new profile process may determine, using the comparing, whether the new profile information is a possible fake profile of a legitimate profile.