Privacy Information Hiding in Screenshots via Category Analysis
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Solution Overview
Problem
Existing methods for hiding privacy information in images are inadequate for screenshots and webpage screenshots, as they perform fuzzification processing on all characters, which is not suitable for hiding only necessary privacy information.
Innovation Solution
A method and device that analyze the information category of privacy information using preset regular expressions or semantic analysis to determine whether to hide specific types of information, such as text or face information, allowing for personalized hiding processing based on user settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fuzzification processing is performed on all characters in a picture, then privacy information is hidden, but picture quality is compromised and non-privacy information is unnecessarily obscured
Solution Approach 1:
The patent applies different processing treatments to different regions of the picture based on their content characteristics. Privacy-sensitive regions (containing phone numbers, IDs, addresses) undergo fuzzification processing, while non-privacy regions (titles, body text, images) maintain original quality. This selective approach ensures privacy protection where needed while preserving overall picture quality.
Solution Approach 2:
The picture is divided into multiple regions through text detection and classification. Each region is independently analyzed to determine whether it contains privacy information. This segmentation allows the system to apply processing only to specific regions containing sensitive data rather than the entire picture, thus maintaining quality in non-privacy areas.
2Reliability
If manual image software is downloaded to hide privacy information, then privacy protection is achieved, but user operation complexity increases
Solution Approach 1:
The system automatically detects, classifies, and processes privacy information in pictures without requiring user intervention. The automatic classification module identifies privacy-sensitive content, and the processing module applies appropriate treatments autonomously. This self-service mechanism eliminates the need for users to manually download or operate specialized image software.
Solution Approach 2:
The system performs privacy information processing in advance before the picture is shared or published. By pre-processing the image to hide sensitive information, the system ensures privacy protection is already in place when the user shares the picture, eliminating the need for users to take additional actions later.
3Reliability
If all character information is hidden, then privacy information is protected, but information completeness is lost
Solution Approach 1:
Different processing intensities are applied to different text regions based on their privacy sensitivity. Titles and body text in non-privacy regions maintain original clarity, while only specific regions containing sensitive information (phone numbers, IDs, addresses) undergo fuzzification. This ensures information completeness is preserved in non-sensitive areas while protecting privacy where needed.
Solution Approach 2:
Instead of applying full fuzzification processing to all text, the system applies partial processing only to regions containing privacy information. This partial action approach protects privacy effectively while minimizing information loss in non-privacy areas, maintaining the overall completeness and readability of the picture.
Data Source
Figure 1~2A
Figure 2B~2C
Figure 3~4
AI summary
The present disclosure relates to a method and a device for hiding privacy information, which belongs to the field of image processing. The method for hiding privacy information includes the steps of: recognizing (101) at least one piece of privacy information in a picture; analyzing (102) an information category for each piece of the privacy information; and performing (103) hiding processing to the privacy information in the picture according to the information category.