Steganographic Privacy Protection for Screens
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Solution Overview
Problem
Current solutions for data extraction from images are time-consuming, prone to human error, and require manual calibration, leading to non-uniform data output, while conventional physical privacy screen protections reduce screen brightness and increase power consumption.
Innovation Solution
A dynamic digital solution using a situationally aware system that applies random image data to user device screens via steganographic techniques, selectively obscuring sensitive information based on user preferences and geolocation, using convolutional neural networks for encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical privacy screen protection is used, then privacy protection is improved, but screen brightness is reduced and power consumption increases
Solution Approach 1:
The patent creates a digital copy of the privacy protection function through steganographic techniques. Instead of using physical screens, the system embeds randomized pixel data into the displayed image, creating a digital replica of privacy protection that can be applied and removed software-based, avoiding the energy costs of physical solutions.
Solution Approach 2:
The system dynamically changes the parameter of image data by embedding randomized pixel values into the displayed content. This transformation allows the same display to provide privacy protection when needed while maintaining normal brightness and power consumption characteristics when privacy mode is not active.
2Measurement precision
If manual data extraction and calibration is performed, then data extraction capability is improved, but time consumption and human error increase
Solution Approach 1:
The patent implements self-service through automated machine learning models that perform data extraction and calibration without human intervention. The system automatically identifies, extracts, and calibrates data from images using trained algorithms, eliminating the need for manual operations while maintaining high accuracy and reducing time consumption.
Solution Approach 2:
The system replaces manual mechanical calibration processes with automated computational algorithms. Machine learning models automatically perform the calibration tasks that would otherwise require human operators to manually adjust parameters, substituting computational automation for manual mechanical adjustment.
3Reliability
If physical privacy screens are installed, then privacy protection is improved, but device portability and aesthetics are reduced
Solution Approach 1:
The patent creates a software-based copy of the privacy protection function that eliminates the need for physical screen attachments. By embedding privacy functionality directly into the digital image data through steganography, the system provides the same protection without adding any physical weight or bulk to the device.
Solution Approach 2:
The system extracts the privacy protection function from its traditional physical form and separates it into a digital component. By taking the privacy function out of the physical domain and implementing it through software algorithms, the system eliminates the weight and portability issues associated with physical privacy screens.
Data Source
AI summary
The present invention generally relates to the field of automated and flexible information extraction and protection for graphical data. In particular, the invention provides a unique platform for analyzing, classifying, extracting, and processing information from images using deep learning image detection models. Embodiments of the inventions are configured to provide an end to end automated solution for intelligently hiding or obscuring private data from graphical displays via the use of embedded steganographic image data techniques.