Context-Aware Brand Masking in Objectionable Video Frames
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Solution Overview
Problem
Existing systems fail to effectively prevent viewers from associating brands and businesses with objectionable content, leading to potential negative impacts on their reputation, without relying on content ratings or central distribution protocols.
Innovation Solution
A frame-by-frame analysis is performed to identify and mask entity identifiers in media content based on contextual comparison between the content and the entity, allowing localized and targeted masking actions to prevent such associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional content rating systems and central distribution protocols are used to mask identifiers, then brand protection is improved, but system complexity and operational constraints increase
Solution Approach 1:
The system performs autonomous frame-by-frame analysis and contextual comparison without requiring external content ratings or central distribution protocols. The AI model independently determines when and what to mask, enabling the system to protect brands while operating autonomously without complex external coordination infrastructure.
Solution Approach 2:
The system segments the content processing into individual frame analysis units, evaluating each frame independently for objectionable content and visible identifiers. This segmentation allows localized masking decisions without requiring system-wide coordination or complex centralized control mechanisms.
2Measurement precision
If comprehensive frame-by-frame analysis is performed to identify entity identifiers, then masking accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary detection of both objectionable content and entity identifiers across all frames before making masking decisions. This preliminary action allows the system to pre-identify all elements requiring masking, enabling efficient processing and reducing overall processing time while maintaining comprehensive accuracy.
Solution Approach 2:
The system applies masking only to specific frames where both objectionable content and visible identifiers coexist, rather than masking all identifiers throughout entire content. This partial action approach reduces unnecessary processing and computational resources while maintaining high masking accuracy where needed.
3Reliability
If contextual comparison between content and entity identifier is performed, then false masking is reduced, but computational complexity increases
Solution Approach 1:
The system applies different analysis depths to different frames based on local context. Frames containing both objectionable content and visible identifiers receive full contextual comparison, while other frames receive simplified processing. This local quality approach maintains high reliability for critical masking decisions while reducing overall computational complexity.
4Reliability
If localized and targeted masking actions are applied, then brand reputation protection is improved, but content modification complexity increases
Solution Approach 1:
The system extracts and masks only the specific entity identifiers that appear alongside objectionable content, leaving the rest of the content unchanged. This extraction approach provides targeted reputation protection while minimizing content modification complexity by affecting only the necessary elements.
Data Source
AI summary
Systems and methods are presented herein for selectively preventing display of brand and business identifiers in content that is considered offensive, objectionable, disturbing, and/or unsettling, to prevent an unintended association by viewers of the content between the content and the brand or business. A request to display media content, comprising at least one frame with objectionable subject matter, is received. In response to determining the at least one frame comprises objectionable subject matter, a determination is made as to whether an entity identifier is visible in the at least one frame. In response to determining at least one entity identifier is visible in the at least one frame, a first context of the objectionable subject matter and a second context of the entity identifier are determined. The first context is compared to the second context and, based on the comparing, a masking action is determined to be required for the entity identifier.