Video Watermark Detection Using Averaged Frames for Moderation
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Solution Overview
Problem
Online platforms face challenges in efficiently identifying and moderating copyrighted or prohibited content due to the vast amount of user-uploaded media, necessitating improved automation for content moderation.
Innovation Solution
A system and method for automatically detecting and tagging media content using a pipeline of modular pipelines, including face detection, watermark identification, and text detection, to flag content requiring moderation, utilizing a text detection model on averaged frames to identify watermarks across multiple frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automation is used to perform preliminary identification of uploaded media, then productivity of content moderation is improved, but measurement precision of content identification may deteriorate
Solution Approach 1:
The content moderation system is divided into multiple specialized pipelines including watermark detection pipeline, text detection pipeline, and face detection pipeline. Each pipeline handles specific content identification tasks independently, allowing the system to process large volumes of content efficiently while maintaining high accuracy through specialized detection algorithms for each content type.
2Measurement precision
If multiple detection pipelines are implemented to identify different content types, then measurement precision of content identification is improved, but device complexity increases
Solution Approach 1:
The system employs a universal moderation interface that can handle multiple content types (watermarks, text, faces) through a single unified architecture. The interface accepts various input formats and coordinates multiple detection pipelines, allowing the system to maintain high identification accuracy across different content types while presenting a simplified, consistent interface to users and reducing overall system complexity.
3Measurement precision
If watermark detection is performed across multiple video frames, then measurement precision of watermark identification is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by detecting watermarks in sampled frames first, then uses the detected watermark information to guide subsequent analysis of remaining frames. This preliminary detection approach allows the system to identify watermarks with high accuracy across multiple frames while significantly reducing processing time by avoiding exhaustive analysis of every single frame.
Data Source
AI summary
Systems, methods, and computer-readable storage media for identifying media content, and more specifically to automatically detecting and tagging media content (such as speech, watermarks, predetermined actions) and flagging portions of content which may require additional moderation. To detect watermarks, a system can receive a video, then sample frames from that video. The system can then average the sampled frames together, resulting in an averaged frame, and execute a text detection model on the averaged frame, resulting in a text detection model output. The system can then identify, based on the text detection model output, a watermark found across the plurality of frames.