GAN Key Generation Network for Image Encryption
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual design of stream cipher generators for image encryption is time-consuming and requires specialized knowledge, and existing methods lack an efficient way to automatically generate key sequences with desired encryption performance.
Innovation Solution
A deep learning generative adversarial network is used to generate keys with larger key space, pseudo-randomness, and sensitivity to initial values, where the network learns to convert input images into chaotic sequences suitable for encryption, employing a generator and discriminator network with specific convolutional layers and loss functions to achieve the desired key characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual design methods are used for stream cipher generators, then encryption performance can be achieved, but the design process is time-consuming and requires specialized knowledge
Solution Approach 1:
The system uses automated deep learning models (GANs) to generate key sequences independently without requiring manual cryptographic design expertise. The generator network automatically learns to produce encryption keys with desired statistical properties, eliminating the need for human experts to manually design and evaluate each key generator
Solution Approach 2:
The patent replaces manual cryptographic design processes with automated machine learning systems. Instead of human experts manually designing key generators based on mathematical theory, the system uses neural networks trained on encryption performance metrics to automatically generate and optimize key sequences
2Manufacturing precision
If manual design process is used to determine encryption performance, then key generator can be evaluated, but the process is capricious and not direct
Solution Approach 1:
The system implements automated feedback loops where the generator network produces key sequences, the discriminator network evaluates their statistical properties and encryption performance, and the generator is retrained based on this feedback. This creates a direct, iterative optimization process that continuously improves key quality based on measured performance metrics
Solution Approach 2:
The discriminator network serves as an intermediary that objectively evaluates the quality of generated key sequences. Instead of relying on subjective manual assessment, the discriminator automatically measures statistical properties and provides quantitative feedback on encryption performance, making the evaluation process more precise and systematic
3Ease of manufacture
If deep learning model parameters are randomly initialized, then model can be trained, but parameters become extremely random which may affect key quality
Solution Approach 1:
The system performs preliminary training phases where the generator network is first trained to produce statistically valid key sequences before being deployed for actual key generation. This preliminary training ensures that the randomly initialized parameters are properly optimized to generate high-quality encryption keys with desired statistical properties
Solution Approach 2:
The system dynamically adjusts training parameters and optimization strategies during the deep learning training process to ensure that randomly initialized parameters converge to values that generate high-quality key sequences. This includes modifying learning rates, training epochs, and loss function weights to maintain key quality throughout training
Data Source
AI summary
A key generation method based on a deep learning generative adversarial network includes: preparing a training set image; construction of a key generation network: constructing the key generation network according to a generator network and a discriminator network, and inputting the training set image to the key generation network; and training of the key generation network: training the key generation network by a deep learning method to generate a key.