Private Video Detection Using Two-Stage Resolution Screening
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Solution Overview
Problem
Existing video processing-based methods fail to efficiently detect explicit content on electronic devices due to computational limitations, inability to handle various semantic features, and issues with far-field or occluded identifiers, making it impractical to classify videos as private or not safe for work (NSFW) on-device.
Innovation Solution
A method and system that utilize a resolution regressing engine to reduce frames to a minimum resolution, analyze for private indicators, and then switch to higher resolution for confirmation, using deep learning models to classify videos as private based on the presence of indicators like obscenity or private body parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video frames are analyzed at high resolution to accurately detect private content, then detection precision is improved, but computational load and processing time increase significantly
Solution Approach 1:
The video analysis process is segmented into two stages: first, low-resolution screening of all frames to identify potential private content; second, high-resolution verification only for frames flagged in the first stage. This segmentation allows the system to maintain high detection precision while significantly reducing overall computational load by applying intensive processing only where necessary.
Solution Approach 2:
The system applies full high-resolution analysis only partially - specifically to frames that contain suspected private indicators detected during low-resolution screening. Most frames receive only low-resolution analysis, which is sufficient for the majority of cases and avoids unnecessary computational expenditure on frames that don't require detailed examination.
2Reliability
If all video frames are processed at high resolution to ensure accurate detection, then detection reliability is improved, but energy consumption increases
Solution Approach 1:
The processing pipeline is divided into energy-efficient low-resolution screening and energy-intensive high-resolution verification stages. By segmenting the workload this way, the system achieves reliable detection outcomes while minimizing total energy consumption, as only a small fraction of frames require the energy-intensive high-resolution processing.
Solution Approach 2:
The system performs preliminary low-resolution analysis on all frames before committing to high-resolution processing. This preliminary action filters out the vast majority of frames that don't contain private content, preventing unnecessary energy expenditure on frames that would be quickly dismissed anyway during full analysis.
3Measurement precision
If deep learning models analyze every frame at full resolution, then classification accuracy is improved, but device computational limitations are exceeded
Solution Approach 1:
The computational workload is segmented between two processing modes: low-resolution mode for initial screening and high-resolution mode for final verification. This segmentation enables mobile devices with limited computational resources to perform accurate classification by distributing the computational burden appropriately across different processing stages.
Solution Approach 2:
Full deep learning analysis is applied only partially to frames that pass the low-resolution screening threshold. The system performs minimal processing on most frames and reserves intensive computational resources only for the small subset of frames that require detailed examination, making the overall process feasible on resource-constrained devices.
4Productivity
If low resolution analysis is used to reduce computational load, then processing efficiency is improved, but detection precision deteriorates
Solution Approach 1:
The system segments detection tasks into two precision levels: low-resolution detection for initial screening and high-resolution detection for confirmation. This segmentation allows the system to achieve high processing efficiency through low-resolution analysis while maintaining high detection precision through selective high-resolution verification of suspicious frames.
Solution Approach 2:
High-resolution analysis is applied partially only to frames where low-resolution analysis detected potential private content. This partial application of high-resolution analysis compensates for the precision limitations of low-resolution screening, ensuring that no true positives are missed while maintaining overall processing efficiency.
Data Source
AI summary
A method for detecting a private video may include estimating a low (e.g., minimum) resolution for performing analysis of a plurality of frames of a video. The method may include reducing a first resolution associated with the plurality of frames to the low (e.g., minimum) resolution. The method may include detecting a presence of at least one indicator for private information in one or more frames amongst the plurality of frames upon analyzing the plurality of frames at the low (e.g., minimum) resolution. The method may include detecting a presence of the private information in the video upon analyzing the one or more frames comprising the at least one indicator at a second resolution in response to detecting the presence of the at least one indicator at the low (e.g., minimum) resolution. The method includes classifying the video as the private video in response to detecting the presence of the private information in the video at the second resolution.