Image Safety Detection Using Anthropometric Heuristics
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Solution Overview
Problem
Existing image safety detection systems have high false alarm rates and are not well-suited for detecting non-pornographic objectionable content, as they are typically tuned to detect pornography and do not accommodate context-specific or flexible definitions of image safety, which is crucial for applications like in-image advertising or content suitability for specific audiences.
Innovation Solution
A system utilizing heuristics based on anthropometry to determine image safety, which includes skin detection, facial feature analysis, and key body area identification, allowing for context-specific and flexible definitions of image safety by analyzing image data to assess appropriateness for various uses and demographics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pornographic image detection techniques are used, then detection accuracy for pornographic images is improved, but false alarm rate increases and adaptability to other objectionable content decreases
Solution Approach 1:
The system implements a universal image safety detection framework that can detect multiple types of objectionable content (pornographic images, violent images, inappropriate content for specific audiences) using a single integrated system. The detection module is designed to be multi-functional, accommodating different detection algorithms and safety criteria through configurable parameters and modular architecture, allowing the same system to serve various content safety needs without requiring separate specialized systems for each content type.
Solution Approach 2:
The system employs dynamic and configurable detection thresholds, safety criteria, and algorithm parameters that can be adjusted based on the specific type of content being analyzed and the desired safety level. The detection sensitivity and criteria can be modified in real-time to adapt to different contexts, such as changing from strict pornographic detection mode to more lenient general content appropriateness mode, thereby reducing false alarms while maintaining detection accuracy for actual violations.
2Reliability
If strict detection criteria are applied to reduce false alarms, then false alarm rate decreases, but detection coverage for subtle objectionable content decreases
Solution Approach 1:
The detection process is divided into multiple sequential stages with progressively stricter criteria. Initial broad detection identifies potential objectionable content using lenient criteria to ensure high detection coverage, then subsequent refinement stages apply stricter criteria to confirm actual violations and eliminate false alarms. This segmented approach allows the system to cast a wide net initially while maintaining reliability through progressive filtering.
Solution Approach 2:
The system incorporates feedback mechanisms where detection results are continuously evaluated and used to adjust detection parameters. When false alarms are detected, the system learns to adjust thresholds and criteria to reduce future false positives while maintaining detection sensitivity. This feedback loop enables the system to optimize the balance between reliability and detection coverage based on accumulated experience and performance metrics.
Data Source
AI summary
Systems and methods are provided for determining the safety of an image, which may be used to determine whether an image is appropriate for a given purpose or for use in a given context. Determining the safety of the image may include analyzing the image to determine the amount of skin exposed in various key body areas of each human represented in the image, such as a photograph.