Pre-filtering Digital Content Using Skin Tone Detection
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Solution Overview
Problem
Existing digital content systems face computational inefficiencies and high network bandwidth usage in detecting and filtering out inappropriate image content, such as child pornography, due to reliance on computationally expensive verification services.
Innovation Solution
A digital content system pre-filters image content by analyzing pixel classifications and skin tone detection, using predefined thresholds and heuristics, and verifies suspicious content with a third-party service using pre-calculated bit strings to reduce network load and service roundtrip time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computationally expensive verification services are used to detect inappropriate image content, then detection reliability is improved, but computational cost and network bandwidth usage increase
Solution Approach 1:
The patent applies preliminary action by implementing a pre-filtering mechanism that performs initial content assessment before verification services are invoked. The pre-filter analyzes basic image properties and compares content against known safe content patterns, allowing the system to identify and block obviously inappropriate content without requiring computationally expensive verification service calls, thus reducing overall computational cost while maintaining detection reliability
Solution Approach 2:
The patent segments the content detection process into multiple stages: a lightweight pre-filtering stage that performs initial assessment using minimal computational resources, and a verification stage that is only invoked for content that passes or fails the pre-filter. This segmentation allows the system to handle the majority of content efficiently while maintaining high detection reliability for suspicious content through selective use of verification services
2Reliability
If verification services are used for all image content, then detection reliability is improved, but network bandwidth usage increases
Solution Approach 1:
The patent applies partial action by invoking verification services only for a subset of content that requires deeper analysis. The pre-filter identifies content that needs verification based on simple criteria, and only those specific items are submitted to verification services. This partial approach maintains detection reliability for content that requires it while significantly reducing overall network bandwidth usage compared to verifying all content
Solution Approach 2:
The patent extracts the verification step from the universal content processing flow and applies it selectively only to content that the pre-filter identifies as potentially inappropriate. By taking out the expensive verification operation from the general processing path and applying it only where necessary, the system maintains high detection reliability for suspicious content while minimizing network bandwidth consumption for content that clearly passes or fails basic checks
3Speed
If pre-filtering with skin tone detection is used, then processing speed is improved, but false positive rate increases
Solution Approach 1:
The patent applies feedback by using the pre-filter's skin tone detection results to inform subsequent verification decisions. When the pre-filter detects skin tones, it flags the content for verification service review rather than making a final determination. The verification service then provides feedback on whether the skin tone detection was accurate in context, allowing the system to learn and improve its pre-filter thresholds while maintaining high processing speed through selective verification
Data Source
AI summary
A digital content system enables users of the content system to access, view and interact with digital content items in a safe, efficient and enjoyable online environment. The content system pre-filters an image content item and determines whether the content item is suspicious of having unsafe content, e.g., nudity and pornography. For example, the content system pre-filters an image content item based on the source of the image content item. A content item from a source known for providing safe content is determined to be safe. The content system determines an image content item to be safe if the content item matches a content item known to be safe or if the content item contains less than a threshold amount of human skin. The content system may further verify the content of the image content item with a verification service and takes remedial actions based on the verification result.


