Image Encryption Project
Project Overview
This is a cryptography project that implements image encryption using various chaos maps and compares their merits based on key sensitivity, adjacent pixel autocorrelation, and intensity histograms. The chaos maps implemented include Arnold cat maps, Henon maps, and Logistic chaos maps, providing a comprehensive analysis of chaos-based encryption techniques.
Key Features
- Three chaos map implementations: Arnold Cat, Henon, and Logistic maps
- Image encryption and decryption with chaos-based algorithms
- Key sensitivity analysis for security evaluation
- Adjacent pixel autocorrelation analysis for encryption quality
- Intensity histogram analysis for ciphertext randomness
- Performance comparison between different chaos map approaches
- Google Colab integration with detailed documentation
Technologies Used
Project Impact
This project addresses the limitations of traditional encryption methods (AES/RSA) for images, which often require high computational power and time for large images. Chaos-based algorithms provide a good combination of speed, high security complexity, and low computational overheads. The project helps researchers understand the trade-offs between security, performance, and key sensitivity in chaos-based image encryption algorithms.
Learning Outcomes
- Advanced chaos theory and nonlinear dynamical systems
- Chaos map implementations for cryptographic applications
- Image encryption quality evaluation methodologies
- Key sensitivity analysis and security assessment
- Statistical analysis of encrypted images (histograms, autocorrelation)
- Performance benchmarking of cryptographic algorithms
- Research documentation and technical writing