Deep Learning Model for Image Information Concealment
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Solution Overview
Problem
Current methods for securing information, such as encryption, do not effectively conceal data within images without compromising the image's readability or requiring complex key exchanges between senders and recipients.
Innovation Solution
A deep learning model is trained to encode and conceal information within images by generating a blended image that minimizes distortion and reconstruction error, allowing the hidden data to be independently concealed in various background images without retraining, using a convolutional autoencoder architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If encryption is used to secure information, then information security is improved, but the process requires complex key exchanges between senders and recipients
Solution Approach 1:
The patent extracts the security mechanism from traditional encryption key exchange and relocates it to the image processing domain. Instead of exchanging keys separately, the security function is embedded directly into the image encoding/decoding process through the deep learning model, eliminating the need for separate key exchange protocols
Solution Approach 2:
The deep learning model serves multiple functions simultaneously: it acts as both the security mechanism (replacing encryption keys) and the image processing tool (encoding/decoding information). This multi-functionality eliminates the need for separate key management systems while maintaining information security
2Reliability
If information is concealed within images, then information security is improved, but the image readability or quality is compromised
Solution Approach 1:
The patent applies partial action by embedding information only in specific regions or pixels of the image that are less critical to overall readability. The deep learning model strategically places encoded data in areas where distortion is less perceptible, maintaining image quality while achieving information concealment
Solution Approach 2:
The model dynamically adjusts encoding parameters such as distortion level, embedding strength, and pixel modification intensity based on the importance of different image regions. This allows the system to maintain optimal balance between information security and image quality by modifying less critical parameters in important areas
3Adaptability or versatility
If a deep learning model is trained to conceal information independently of background images, then adaptability is improved, but training complexity increases
Solution Approach 1:
The patent applies preliminary action by training the deep learning model in advance on a diverse set of background images with various characteristics (colors, patterns, resolutions). This pre-training enables the model to learn generalizable features and achieve background independence without requiring retraining for each new background, thus reducing overall complexity
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a method for training a deep learning model that includes encoding a content item; generating a blended image by combining a background image and the encoded content; decoding the blended image to generate decoded content corresponding to the content item; and defining or specifying a loss function related to the deep learning model. The method also includes determining values of training parameters for the deep learning model to minimize the loss function, thereby obtaining a trained deep learning model. The method also includes an information concealing procedure using the trained deep learning model to conceal user content by encoding the user content and blending the encoded user content with a user-selected image; the information concealing procedure is substantially independent of the user-selected image. Other embodiments are disclosed.