Source : Verilog HDL

Only Simulation model Cost : Rs. 15,000/- 
With Hardware TFT Integration + FPGA Board: Rs. 30,000/-

Proposed Abstract:

Image encryption plays a crucial role in securing digital communication, especially with the rise of cyber threats and data breaches. This research focuses on implementing a Chaos-based Pseudorandom Number Generator (PRNG) for image encryption and compares its performance with Fibonacci and Galois-based Linear Feedback Shift Registers (LFSRs). The proposed system is developed using Verilog HDL and synthesized on a Xilinx Spartan-6 FPGA, with a real-time TFT display interface for encrypted and decrypted image visualization. Traditional LFSR-based PRNGs are widely used due to their simplicity and speed; however, they suffer from predictable periodicity and lower security strength. In contrast, Chaos-based PRNGs provide higher randomness and security, making them ideal for cryptographic applications. In this work, different PRNG approaches are analyzed based on randomness quality using the NIST test suite, hardware resource utilization (LUTs, FFs, power consumption), and encryption security (correlation, entropy, and key sensitivity). The Chaos-based PRNG is then integrated into a stream cipher encryption system, where image pixels are transformed using bitwise XOR and chaotic substitution-permutation operations. The encrypted images are decrypted using the inverse transformation and displayed on a TFT display, ensuring real-time validation. Experimental results confirm that the Chaos-based PRNG outperforms LFSR-based PRNGs in security strength and randomness, while maintaining efficient FPGA resource utilization. This work demonstrates a practical hardware-based image encryption system, suitable for real-time, secure multimedia applications such as IoT, medical imaging, and defense systems. Future enhancements include optimizing chaos-based PRNGs for high-speed cryptographic applications and exploring AI-based encryption techniques for enhanced security.

List of the following  materials will be included with the Downloaded Backup: