Fine-Grained Image Moderation Ontology for Accurate Content Filtering
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Solution Overview
Problem
Existing content moderation systems for user-generated images on online platforms are resource-intensive and lack accuracy, often relying on binary ratings that are inadequate for complex applications, as they require human moderation or community flagging.
Innovation Solution
An automated content moderation system using neural networks with a fine-grained and dynamic image classification ontology, allowing for customizable categorization of image content into multiple categories, reducing false positives and negatives through granular control and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human moderators review uploaded image content, then content moderation accuracy is improved, but resource consumption and time cost increase
Solution Approach 1:
The patent segments the content moderation task into multiple fine-grained categories (e.g., violence, pornography, hate speech, and their subcategories) rather than a single binary classification. This segmentation enables automated systems to handle specific categories efficiently while reducing the burden on human moderators who only need to review cases that automated systems cannot confidently classify.
Solution Approach 2:
The patent introduces an automated content moderation system as an intermediary between users and human moderators. This intermediary handles the majority of moderation tasks automatically, filtering out clear cases and only presenting ambiguous or complex cases to human moderators, thereby significantly reducing resource consumption while maintaining accuracy.
2Device complexity
If binary rating systems are used for image content, then system complexity is reduced, but moderation accuracy and applicability to complex scenarios deteriorate
Solution Approach 1:
The patent divides the content moderation system into multiple specialized classifiers, each trained to detect specific types of inappropriate content (e.g., violence detection, pornography detection, hate speech detection). Each classifier outputs a independent rating, and the final decision is made by aggregating these ratings. This segmentation maintains relative system simplicity while dramatically improving accuracy for complex scenarios.
Solution Approach 2:
The patent creates a universal content moderation framework that can handle multiple types of inappropriate content through a single integrated system. The system uses a common architecture with specialized components that can be configured for different platforms and requirements, providing multi-functional capability without proportionally increasing complexity.
3Loss of energy
If community moderation is implemented, then resource consumption is reduced, but user experience and moderation reliability worsen
Solution Approach 1:
The automated content moderation system serves as an intermediary that processes community reports and moderations more reliably and consistently than manual community moderation. It provides a standardized evaluation framework that reduces the variability and unreliability associated with community-based moderation while maintaining low resource consumption.
Solution Approach 2:
The patent implements feedback mechanisms where the automated system learns from community moderation decisions and user reports. The system continuously refines its classification models based on feedback data, improving reliability over time while maintaining efficient resource usage compared to purely community-driven moderation.
4Measurement precision
If fine-grained image classification is implemented, then content moderation precision is improved, but computational resources and processing time increase
Solution Approach 1:
The patent segments the classification task into a hierarchical structure with coarse-grained categories first (e.g., violence, pornography, hate speech) and then applies fine-grained classification only to images that fall into restricted categories. This two-stage segmentation approach achieves high classification precision for critical content while minimizing processing time for benign images.
Solution Approach 2:
The patent applies fine-grained classification partially - only to images that are likely to be inappropriate based on initial filtering. Instead of applying computationally intensive fine-grained analysis to all images, the system performs partial classification on a subset of images, achieving high precision for moderation-critical cases while reducing overall processing time.
Data Source
AI summary
The present disclosure provides for customizable content moderation using neural networks with fine-grained and dynamic image classification ontology. A content moderation system of the present disclosure may provide a plurality of image categories in which a subset of of image categories may be designated as restricted categories. The restricted categories may be chosen by a content provider or an end-user. The content moderation system may utilize a neural network to classify image data (e.g., still images, video, etc.) into one or more of the plurality of image categories, and determine that an image is a restricted image upon classifying the image into one of the restricted categories. The restricted image may by flagged, rejected, removed, or otherwise filtered upon being classified as a restricted image.