Overfit Text-to-Image Model for Secure Data Compression
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Solution Overview
Problem
Large-scale image datasets are cumbersome to transfer and share due to their size, and they often contain sensitive information that is at risk of exposure during transmission and training processes.
Innovation Solution
The use of overfitting techniques on pretrained image models for large-scale image data encryption and compression, where a text-to-image machine learning model is trained to reproduce images from a training dataset based on natural language descriptions, allowing for efficient compression and encryption of image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large-scale image datasets are transferred and shared, then more data is available for training, but the data size becomes cumbersome and transmission time increases
Solution Approach 1:
The patent creates a compressed representation of the image dataset by training a text-to-image model to reproduce images from natural language descriptions. Instead of transferring the original large-scale image datasets, the system transfers the compressed model parameters and textual descriptions, which can be used to regenerate the images. This copying approach maintains data accessibility while dramatically reducing transmission size and time.
2Quantity of substance
If large-scale image datasets are transferred, then more data is available for training, but data security and privacy are at risk during transmission
Solution Approach 1:
The system creates a compressed copy of the image data in the form of a trained text-to-image model. The original images are not transmitted or stored in their original form, but rather the model parameters and textual descriptions are shared. This copying approach maintains the ability to access and use the image data while eliminating the security risks associated with transmitting and storing large-scale original image datasets.
3Quantity of substance
If image data is compressed using traditional methods, then data size is reduced, but compression loss increases and quality deteriorates
Solution Approach 1:
The patent changes the fundamental parameters of data representation by transforming images into a text-to-image model format. Instead of using traditional compression algorithms that reduce pixel data, the system represents images through learned textual descriptions and model parameters. This parameter transformation achieves high compression ratios while maintaining image quality, as the model can reproduce images from the compressed representation with minimal loss.
Data Source
AI summary
Train a text-to-image machine learning model by using a training dataset. The training dataset includes images and respective natural language descriptions of those images. The training causes the text-to-image machine learning model to be overfit on the training dataset. Store the trained overfit text-to-image machine learning model. Submit a given natural language description to the stored overfit text-to-image machine learning model. In response to the submitting, receive as output from the overfit text-to-image learning machine learning model an output image. The output image is a reproduction of a corresponding image from the training dataset and corresponds to the submitted natural language description.