This project employs chaotic key sequences for encryption and decryption, enhancing cryptographic security. Chaotic systems generate random-like sequences used as keys to modify pixel values in images. By leveraging chaos theory, this method offers robust encryption.
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Encryption: Chaotic key sequences modify pixel values in images. These sequences, generated from chaotic systems, provide high unpredictability.
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Decryption: Decryption uses the same chaotic sequence by replicating initial conditions and control parameters. Applying reverse operations restores the original image.
Chaotic systems' sensitivity to initial conditions makes this encryption formidable. Even tiny deviations lead to vastly different sequences, rendering decryption challenging.
Web technologies power this project, ensuring cross-platform accessibility. Harnessing chaos theory, the project showcases the power of chaos in Cryptography.
First of all, install Python dependencies
pip install -r requirements.txt
Then Run the Flask server
python app.py
The project will be running on the Werkzeug server
To encrypt an original image, enter the initial condition and control parameter, which act as keys to generate the chaotic key sequence
Each pixel of an image gets many keys for encryption, that is why this algorithm is so robust for CryptoGraphy.
A bifurcation diagram illustrates the values that a system approaches asymptotically (fixed points, periodic orbits, or chaotic attractors) in relation to a bifurcation parameter within the system.
A Bifurcation Diagram reveals how system stability can significantly depend on input factors.
The diagram is generated by iterating a given equation over a defined range of r values for a set number of iterations. The equation's results are then plotted, with each r on the x-axis and the corresponding x values on the y-axis.
The logistic map equation is used:
Logistic Map
Visualization The resulting bifurcation diagram provides insights into the system's behavior as r changes, highlighting patterns and shifts in asymptotic values.
Parameters Minimum r: 3.0 Maximum r: 4.0 Max iterations: 1000 Skip iterations: 100 Step r: 0.0001