Neural Network Video Prurient Activity Detection
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Solution Overview
Problem
Multimedia distribution services face challenges in efficiently performing compliance reviews for video content due to varying regional rules and the increasing volume of submitted content, particularly in identifying prurient activities that may provoke significant sexual interest.
Innovation Solution
A machine learning model, trained using neural networks that analyze video content along a temporal dimension, generates scores indicating the likelihood of prurient activities, allowing for the efficient identification and presentation of relevant video clips for compliance evaluation across different regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual compliance review is performed on each submitted video content, then accuracy in detecting prurient activity can be maintained, but productivity decreases due to the increasing volume of content
Solution Approach 1:
The video content is segmented into smaller clips based on detected prurient activity. The machine learning model identifies specific time segments within videos that contain potentially problematic content, allowing reviewers to focus only on relevant portions rather than watching entire videos, thus improving productivity while maintaining detection accuracy.
Solution Approach 2:
A machine learning model serves as an intermediary between the submitted video content and human reviewers. The model pre-analyzes videos to generate scores indicating likelihood of prurient activity, filtering and prioritizing content for human review based on these scores, thereby increasing review throughput without sacrificing detection accuracy.
2Reliability
If comprehensive compliance review is performed on all video content, then reliability of content distribution can be ensured, but loss of time increases due to varying regional rules
Solution Approach 1:
The system applies different compliance criteria and detection thresholds tailored to specific regional rules. Video content is evaluated according to the particular standards of each target region, allowing for efficient compliance review that adapts to local requirements without performing unnecessary comprehensive checks, thus reducing review time while ensuring reliability.
Solution Approach 2:
The machine learning model performs preliminary analysis of video content to generate prurient activity scores before human compliance review. This preliminary action identifies high-risk segments that require detailed examination, allowing reviewers to focus time on content that actually needs compliance verification, thereby reducing overall review time while maintaining compliance assurance.
3Measurement precision
If machine learning model analyzes temporal dimension of video content, then detection precision of prurient activity improves, but use of energy increases
Solution Approach 1:
The machine learning model analyzes only the temporal dimension of video content rather than performing exhaustive analysis of all video attributes. By focusing computation specifically on temporal patterns that indicate prurient activity, the system achieves high detection precision while minimizing unnecessary computational energy consumption.
Data Source
AI summary
Techniques are disclosed for detecting a type of prurient activity shown by a portion of video content. In an example, a machine learning model of a computer system may receive a second portion of video content, the machine learning model including a neural network that is trained to analyze a temporal dimension of the second portion. The machine learning model determines a score indicating a likelihood that the video content shows the type of prurient activity based in part on applying a three-dimensional filter to the second portion of the video content. The computer system then generates a video clip that includes at least the portion of the video content showing the type of prurient activity based on the score, and provides the video clip for display.