Multimedia Content Recognition for Malicious Promotion Detection
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Solution Overview
Problem
Existing technologies struggle to accurately recognize malicious promotion in multimedia content due to its frequent updates, leading to low recognition accuracy and difficulty in effectively combating such content.
Innovation Solution
A recognition method for multimedia content that comprehensively analyzes title, abstract, image, and audio data using text classification models to determine the extent of malicious promotion, employing deep neural networks, heuristic policies, and dictionary expansion to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text recognition methods are used for malicious promotion detection, then the system is simple to operate, but the recognition accuracy is low due to frequent updates of malicious content
Solution Approach 1:
The system segments malicious promotion detection into multiple independent modules: title text analysis, abstract text analysis, image text recognition (OCR), and audio text transcription. Each module processes specific content types separately and feeds results to a unified classification model, enabling comprehensive detection without overwhelming system complexity
Solution Approach 2:
The system employs a composite recognition policy that integrates multiple analysis dimensions (text, image, audio) with different detection strategies including keyword matching, semantic analysis, and machine learning classification. This composite approach combines the strengths of various methods to achieve high accuracy against diverse malicious content
2Measurement precision
If comprehensive multi-dimensional analysis is performed on video content, then the recognition accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by first extracting and analyzing title and abstract texts, which contain the most prominent malicious promotion indicators. Based on these initial results, the system can prioritize or skip more resource-intensive image and audio analysis for certain content types, reducing overall processing time while maintaining accuracy
Solution Approach 2:
The system implements a tiered analysis approach where not all video content undergoes complete multi-dimensional analysis. For content with clear malicious indicators in title or abstract, full analysis may be skipped. For borderline cases, the system performs excessive analysis including all modalities to ensure accurate detection, optimizing the balance between speed and accuracy
Data Source
AI summary
This disclosure relates to a method for recognizing multimedia content. The method includes: obtaining target text information and content information in a video; performing text recognition processing on the content information to obtain associated text information; when the original text information or the associated text information meets a first malicious promotion condition, obtaining a target text classification result by a text classification model; and determining a video recognition result corresponding to the video according to the target text classification result.