Deep Convolutional Neural Network for Privacy Region Localization
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Solution Overview
Problem
Existing image privacy perception methods lack the ability to accurately identify and locate privacy regions within images, relying on traditional feature extraction methods with limited semantic representation and requiring additional user-defined information, leading to poor generalization and increased computational overhead.
Innovation Solution
An image privacy perception method based on deep learning, utilizing a deep convolutional neural network with transfer learning, bilinear operation layers, and Tensor Sketch algorithm for dimensionality reduction, to extract attention profiles and locate privacy regions within images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image feature extraction methods (SIFT, RGB, color histograms) are used, then the method is simple and computationally efficient, but the feature expression capability is limited and cannot represent deep semantic features
Solution Approach 1:
The patent replaces traditional mechanical/image processing feature extraction methods (SIFT, color histograms) with a deep convolutional neural network system that automatically learns hierarchical features from images, enabling deep semantic feature representation without manual feature engineering
Solution Approach 2:
The patent applies transfer learning by pre-training the CNN on large-scale image datasets before fine-tuning on privacy perception tasks, allowing the model to acquire general visual features in advance and adapt to specific privacy detection requirements with limited data
2Measurement precision
If additional information (user-defined tags, access control policies) is required to achieve acceptable perception effect, then the classification accuracy improves, but the application requirements become harsh and versatility deteriorates
Solution Approach 1:
The patent enables the system to automatically perceive and classify privacy images using the trained deep learning model without requiring user intervention, manual tagging, or access control policies, making the system self-sufficient and universally applicable across different scenarios
Solution Approach 2:
The patent creates a universal privacy perception model through transfer learning that can generalize across different privacy scenarios and image types without requiring scenario-specific configuration or user-defined parameters, enhancing versatility while maintaining accuracy
3Measurement precision
If privacy perception is completed only at image level (distinguishing privacy vs non-privacy), then the method is simple and fast, but the ability to locate and protect specific privacy regions is lost
Solution Approach 1:
The patent segments the image processing task into two stages: first classifying the entire image as privacy or non-privacy, then if privacy is detected, extracting the specific privacy region through attention profile analysis, enabling targeted protection without processing every image at full detail
4Measurement precision
If deep convolutional neural network with bilinear operation layer is used, then the feature expression capability is enhanced, but the computational overhead increases
Solution Approach 1:
The patent applies bilinear operation selectively only to enhance specific feature representations that contribute most to classification accuracy, rather than applying complex operations uniformly across all features, balancing performance improvement with computational cost
Data Source
AI summary
An image privacy perception method based on deep learning, including the following steps: S1, building a privacy classification data set with labeled categories, and training a privacy perception network with a transfer learning method; S2, recognizing a privacy image using a deep convolutional neural network oriented to privacy perception; and S3, extracting an attention profile according to deep convolutional features of the neural network, and locating an attention focusing region to complete the perception of an image privacy region. The method has the following beneficial effects: by completing end-to-end training and testing based on the deep neural network, the privacy image can be accurately distinguished with the privacy region located, facilitating the selective protection of the privacy information in the image.