Multimodal Perception and Agent-Based Decision-Making Integrated Approach for Short Video Content Compliance Regulation
AI-powered multimodal perception and agent-based decision-making significantly enhance short video content compliance regulation, offering superior precision and scalability over traditional methods.
This paper proposes MAI-SVCR, a Multimodal Parsing and Agent-based Decision-Making Integrated Framework for Short Video Compliance Regulation. It tackles challenges in regulating massive short video content by integrating multimodal data fusion for comprehensive content representation and an agent-based decision-making layer for efficient, precise, and multi-category non-compliance detection. The framework comprises a multimodal perception layer (audio, image, text parsing and fusion) and an agent-based decision-making layer (Minor Protection Agent, National Security Agent, Social Order and Good Customs Monitoring Agent, and a Collaborative Decision Agent). Experiments demonstrate MAI-SVCR's superior performance in precision, recall, and F1 score, offering a scalable technical solution for content governance.
Executive Impact & Key Performance Indicators
MAI-SVCR delivers tangible benefits, significantly improving compliance efficiency and reducing operational overhead.
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Compliance F1 Score
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Regulatory Efficiency Boost
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False Positive Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Multimodal Perception Layer
Agent-Based Decision-Making Process
MAI-SVCR Performance Benchmarking
Real-World Impact: Enhancing Regulatory Compliance
Multimodal Perception Layer
The MAI-SVCR system first processes short video content through a Multimodal Perception Layer. This layer extracts and integrates core elements from audio, image, and text. Audio content parsing identifies acoustic events, speech, and sensitive words. Image content parsing uses deep CNNs and OCR to detect violent scenes, contraband, and embedded text. Text content parsing analyzes subtitles and comments for themes, entities, and evasive expressions, leveraging large models and rule libraries. All these features are then fused into a unified semantic representation, providing high-confidence input for decision-making.
Agent-Based Decision-Making Process
The Agent-Based Decision-Making Layer utilizes a multi-agent system built upon regulatory knowledge bases and historical case databases. It includes specialized agents: Minor Protection, National Security, and Social Order/Good Customs Monitoring Agents. A Collaborative Decision Agent then integrates their judgments through a hierarchical arbitration mechanism to output the final compliance verdict. This structured approach allows for precise and comprehensive regulation.
MAI-SVCR Performance Benchmarking
Comparative experiments demonstrate MAI-SVCR's significant advantages over traditional rule-based matching and single large model approaches in key metrics. The system achieves a 94.8% F1 score, outperforming rule-based methods (19.1%) and traditional large models (75.5%). This robust performance is attributed to the synergistic innovation of multimodal perception and agent-based decision-making.
Real-World Impact: Enhancing Regulatory Compliance
A major short video platform faced escalating challenges with content compliance due to massive user-generated content and increasingly covert non-compliant videos. Traditional manual review and single-model AI solutions were inefficient and lacked the precision to handle the volume and complexity.
Agent-Based Decision-Making Process
Multimodal Content Representation
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Minor Protection Agent
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National Security Agent
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Social Order Agent
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Collaborative Decision Agent
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Compliance Judgment Output
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Real-World Impact: Enhancing Regulatory Compliance
A major short video platform faced escalating challenges with content compliance due to massive user-generated content and increasingly covert non-compliant videos. Traditional manual review and single-model AI solutions were inefficient and lacked the precision to handle the volume and complexity.
Problem: The platform struggled to keep pace with an exponential growth in short video content, leading to undetected non-compliant content and high operational costs for manual moderation.
Solution: Implementation of MAI-SVCR, integrating multimodal perception for robust content analysis and an agent-based decision system for hierarchical, precise compliance judgments across various categories (minor protection, national security, social order).
Impact: 94.8% F1 score achieved in compliance detection, significantly reducing undetected non-compliant content and moderator workload. The system provided a scalable and adaptable solution, improving platform governance and ensuring a healthier online ecosystem.
Calculate Your Potential ROI with MAI-SVCR
Estimate the cost savings and efficiency gains your organization could achieve by implementing our AI-powered content compliance solution.
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Your Implementation Roadmap
A structured approach to integrating MAI-SVCR into your enterprise workflows for seamless adoption.
Phase 1: Discovery & Integration
Initial consultation, system requirements analysis, data pipeline integration, and foundational model deployment.
Phase 2: Agent Training & Fine-tuning
Training of specialized compliance agents using your specific regulatory knowledge base and historical data, followed by fine-tuning for optimal accuracy.
Phase 3: Pilot Deployment & Optimization
Staged rollout in a controlled environment, continuous monitoring of performance, and iterative optimization based on real-world feedback.
Phase 4: Full-Scale Operation & Ongoing Support
Full system deployment, comprehensive training for your team, and continuous support with regular updates and performance reviews.
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