Cascaded Moderation Models for Live Streaming Violation Detection
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Solution Overview
Problem
Existing live streaming moderation technologies face challenges in accurately and comprehensively moderating violation content in real-time due to instability in network conditions, leading to issues of false rejection and false acceptance, as they rely on single feature-trained neural networks for visual and audio analysis.
Innovation Solution
A method involving a cascaded high-accuracy and high-recall moderation model for preliminary moderation, followed by a behavior moderation model for secondary analysis, using multi-dimensional features to determine a target violation score, ensuring comprehensive moderation without additional devices and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single feature-trained neural networks are used for moderation, then device complexity is reduced, but measurement precision deteriorates due to false rejection and false acceptance
Solution Approach 1:
The moderation system is segmented into multiple independent neural networks, each trained on different features (visual, audio, text, behavior). These networks process specific feature types and their outputs are combined, allowing each network to remain relatively simple while the overall system achieves high precision through multi-feature integration.
2Measurement precision
If multiple moderation models are cascaded, then measurement precision improves, but productivity deteriorates due to increased processing time
Solution Approach 1:
The system performs preliminary filtering using simpler models first (e.g., visual and audio moderation), and only passes content that exceeds certain thresholds to more complex analysis (e.g., behavior analysis and text moderation). This preliminary action reduces the burden on subsequent models and maintains real-time processing capability while improving overall precision.
Solution Approach 2:
Different parts of the moderation pipeline use models with different complexity levels appropriate to their specific tasks. Simple feature extraction uses lightweight networks, while critical decision points use more sophisticated behavior analysis. This local optimization balances precision requirements with processing speed at each stage.
3Measurement precision
If comprehensive multi-dimensional features are analyzed, then measurement precision improves, but device complexity increases
Solution Approach 1:
The comprehensive feature analysis is segmented across multiple specialized neural networks, each handling a specific feature dimension (visual, audio, text, behavior). This segmentation allows each network to focus on its specific feature type with appropriate complexity, while the system as a whole achieves comprehensive multi-dimensional analysis through aggregation of all network outputs.
Data Source
AI summary
Provided is a method for moderating live streaming. The method includes: performing preliminary violation moderation on a current live streaming frame in a to-be-moderated live streaming room based on a high-accuracy moderation model and a high-recall moderation model that are cascaded with each other; and acquiring a target violation score of the current live streaming frame by inputting a first violation score of the current live streaming frame under the high-accuracy moderation model, a second violation score of the current live streaming frame under the high-recall moderation model, and a multi-dimensional behavior feature of the current live streaming frame in the to-be-moderated live streaming room to a pre-constructed behavior moderation model in response to the current live streaming frame passing the preliminary violation moderation.