Live Video Fraud Detection via Pre-Trained Object Recognition
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Solution Overview
Problem
Current mechanisms for verifying the integrity of live video streams are inadequate in detecting fraudulent annotations and ensuring the authenticity of physical objects presented, often requiring excessive computational power and resulting in delayed or inaccurate fraud detection.
Innovation Solution
A system that uses image recognition models trained on specific physical objects delivered to a presenter, which selectively activates or deactivates the camera function based on whether the streaming video accurately displays these objects, preventing fraudulent representations by blocking unauthorized or incorrect physical objects from being presented.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image recognition models are applied to verify physical objects in live video streams, then fraud detection accuracy is improved, but computational power requirements increase
Solution Approach 1:
The system pre-trains image recognition models offline using large datasets of physical objects. These pre-trained models are then deployed to mobile devices, where they can perform verification with minimal real-time computational overhead. The heavy lifting of model training is done beforehand, allowing accurate fraud detection during live streaming without excessive power consumption.
Solution Approach 2:
The patent introduces an intermediary verification layer between the user and the live video stream. The image recognition model acts as a mediator that automatically analyzes video content and verifies physical object presence, reducing the need for manual verification and minimizing the computational burden on the mobile device by delegating complex analysis to the pre-trained model.
2Measurement precision
If computational mechanisms are enhanced to detect fraud in real-time, then fraud detection accuracy is improved, but processing time increases
Solution Approach 1:
By pre-training image recognition models offline before deployment, the system prepares sophisticated verification capabilities in advance. During live streaming, these pre-trained models can perform rapid object verification without requiring complex real-time computation, thus maintaining high fraud detection accuracy while minimizing processing delays.
Solution Approach 2:
The system dynamically adjusts verification intensity based on risk assessment. For low-risk scenarios, minimal verification is performed to maintain fast processing. For high-risk scenarios, the pre-trained models engage in more thorough analysis. This dynamic approach balances fraud detection accuracy with processing speed requirements.
Data Source
AI summary
A device determines that a physical object is to be presented, by a presenter, in a streaming video. The device identifies a set of physical objects that are confirmed as having been delivered to a physical address of the presenter, accesses an image recognition model that is trained using the set of physical objects, and applies the image recognition model to the streaming video. The device determines, based on output from the image recognition model, whether the streaming video includes the physical object. In response to determining that the physical object is part of the set of physical objects, the device permits the physical object to be presented in the streaming video, and in response to determining the physical object is not part of the set of physical objects, the device blocks the physical object from being presented in the streaming video.