Neural Network Image Recognition with Steganographic Watermark Protection
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Solution Overview
Problem
Current automated systems for data extraction from images are time-consuming, prone to human error, and lack uniformity, often requiring manual calibration and resulting in inefficient data analysis across different image types, while also facing challenges in protecting and regulating the distribution of deep learning models.
Innovation Solution
A system utilizing multiple convolutional neural network layers with steganographic watermarking for secure data extraction and analysis, embedding randomized secret images undetectably within encoded images, and using a stegoanalyser to remove the watermark, allowing for secure distribution and protection of models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If deep learning models are distributed freely for use, then accessibility and adoption improve, but model integrity and control over distribution deteriorate
Solution Approach 1:
The patent embeds a steganographic watermark layer within the neural network model itself, creating a nested structure where the protective mechanism is contained inside the protected asset. This allows the model to be distributed freely while the embedded watermark remains hidden within the model weights, preventing unauthorized copying and maintaining distribution control without affecting accessibility
Solution Approach 2:
The steganographic watermark acts as an intermediary between the model distributor and the end user. It provides a hidden verification mechanism that allows distributors to track and control model usage without interfering with the normal operation or accessibility of the model for authorized users
2Measurement precision
If manual calibration is performed for data extraction, then measurement precision improves, but productivity and time efficiency deteriorate
Solution Approach 1:
The patent performs preliminary calibration by training the neural network model with ground truth data before deployment. The model learns the correct extraction and alignment parameters during training, so that when deployed, it automatically performs accurate data extraction without requiring manual calibration for each new dataset, thus maintaining precision while improving productivity
Solution Approach 2:
The neural network model performs self-calibration by automatically learning optimal extraction parameters from training data. Instead of requiring manual intervention for each extraction task, the model adapts and configures itself during training, enabling both high precision and efficient automated processing of diverse image types
3Reliability
If steganographic watermarking is embedded in neural network models, then model protection improves, but model complexity and processing overhead worsen
Solution Approach 1:
The patent merges the watermark embedding process with the neural network training process itself. The watermark is integrated into the model weights during training, combining the protective mechanism with the model creation process. This eliminates the need for separate watermark embedding steps and reduces overall system complexity while maintaining strong protection
Solution Approach 2:
The patent modifies the parameter space of the neural network by embedding watermark information within the weight parameters. By encoding protection mechanisms within the existing model parameters rather than adding separate protective layers, the system achieves model protection without significantly increasing computational complexity or processing overhead
Data Source
AI summary
Embodiments of the invention are directed to systems, methods, and computer program products for a unique platform for analyzing, classifying, extracting, and processing information from images. In particular, the novel present invention provides a unique platform for analyzing, classifying, extracting, and processing information from images using deep learning image detection models with the use of convolutional neural networks. Embodiments of the inventions are configured to provide an end to end automated solution for extracting data from images that can be securely distributed to multiple entities and third parties while maintaining the ability to deter unauthorized copying of the platform via embedded steganographic watermarking introduced during the encoding process and only removable using an additional step between the encoding and decoding processes.