In this project, I developed a chaos-driven image encryption framework leveraging the Arnold Cat Map and Henon Map to enhance image security through nonlinear confusion and diffusion mechanisms. The Arnold Cat Map was implemented for pixel permutation, scrambling spatial relationships while maintaining intensity values, whereas the Henon Map generated a chaotic XOR mask to diffuse pixel intensities, effectively flattening histograms and increasing entropy. The system was built using Python (NumPy, OpenCV, Matplotlib), with mathematical formulations for both maps implemented from scratch.

This work demonstrates how chaotic dynamics can form lightweight yet secure cryptographic primitives suitable for real-time image encryption in fields like telemedicine and visual surveillance.