Ad Fraud Detection via Pixel Color Resemblance
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Solution Overview
Problem
Current ad fraud detection systems rely on client-side verification, which is resource-consuming and prone to workarounds, failing to effectively prevent fraudulent representation of online advertisement impressions, clicks, or conversions.
Innovation Solution
A system and method that determine color content resemblance of ad media pixels across multiple user devices by calculating content resemblance scores and averaging them to determine if the average score falls within a predefined threshold range, thereby detecting potential ad fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If client-side verification is used to detect ad fraud, then ad fraud detection capability is provided, but resource consumption increases and the system becomes prone to workarounds
Solution Approach 1:
The patent introduces a server as an intermediary between user devices and advertisers. The server receives pixel color data from user devices, calculates content resemblance scores by comparing actual colors with expected colors, and determines viewability metrics. This centralizes the verification logic on the server side, reducing resource consumption on client devices while maintaining detection capability.
Solution Approach 2:
The patent replaces complex client-side verification mechanisms with a simplified color-based verification system. Instead of requiring full ad rendering verification on client devices, the system only needs to capture and transmit pixel color data, which the server then verifies against expected color values. This substitution dramatically reduces client-side computational resources while maintaining fraud detection effectiveness.
2Reliability
If client-side verification is used to detect ad fraud, then ad fraud detection capability is provided, but the system becomes prone to workarounds and hacks
Solution Approach 1:
By moving the verification logic to a server intermediary, the system eliminates the ability of clients to manipulate verification outcomes. The server independently calculates content resemblance scores by comparing received pixel colors with expected colors from ad servers, preventing client-side hacks from affecting the verification result.
Solution Approach 2:
The patent changes the verification parameter from complex ad rendering verification to simple pixel color comparison. This parameter change makes the verification process resistant to workarounds because color values are objective, easily measurable, and difficult to manipulate without detection, compared to complex rendering verification that can be bypassed through various hacks.
3Reliability
If pixel color verification is performed across multiple user devices, then color representation consistency is ensured and ad fraud is detected, but system complexity increases
Solution Approach 1:
The patent applies homogeneity by standardizing the verification process across all user devices. Each device captures pixel colors using the same method, and the server applies uniform color comparison logic against expected values. This homogeneous approach ensures consistent color representation verification while keeping individual device complexity low.
Solution Approach 2:
The system uses color values as simplified copies or representations of the actual ad content. Instead of verifying the complete ad rendering, the system captures essential color information that represents the ad's visual content. This copying approach reduces system complexity by focusing on essential verification attributes rather than complete content verification.
Data Source
AI summary
The presently disclosed subject matter aims to provide a system and method for detecting potential ad frauds by determining color content resemblance of at least one pixel of an ad media displayed within at least one given placement on a plurality of user devices, and a desired color of the at least one pixel.