An Efficient Image Encryption Framework Integrating CPL-IES with Feature-Based Dynamic Key Generation, ECC, and AES

Abstract

This study presents an efficient encryption framework for an image to enhance visual data security. The system combines a Collision-Parity Lightweight Image Encryption Scheme (CPL-IES) with dynamic keys derived from image features, further reinforced by Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) protocols. For establishing a rigorous plaintext-dependence, encryption keys are derived dynamically from intrinsic statistical attributes of the source image, specifically its mean intensity, standard deviation, and information entropy. ECC facilitates the secure establishment of a shared secret between communication endpoints without direct key transmission, while AES is deployed to protect the extracted feature metadata. To counteract statistical and differential cryptanalysis, a hyperchaotic map is leveraged to execute multi-dimensional pixel permutation and diffusion, yielding high cryptographic entropy. The proposed architecture natively accommodates both color and grayscale images by executing independent channel-wise processing. Empirical evaluation across diverse benchmark datasets indicates that the framework guarantees robust security parameters alongside minimized computational complexity, making it highly viable for real-time secure visual data transmission.

Introduction

The text proposes a secure image encryption system designed to protect sensitive images in modern digital environments such as healthcare, military communication, IoT, and cloud storage, where conventional encryption methods are often insufficient due to the large size and strong pixel correlations in images.

The main contribution is a hybrid cryptographic framework that combines:

• Collision-Parity Lightweight Image Encryption Scheme (CPL-IES) with hyperchaotic maps for pixel scrambling and diffusion,
• Dynamic key generation based on image features (mean intensity, standard deviation, entropy),
• A dual security layer using ECC and AES, where ECC handles secure key exchange and AES protects image feature data.

The system works by extracting image features to generate a unique dynamic key, which is tightly bound to the input image. This makes the encryption highly sensitive to even minor image changes and resistant to attacks. A hyperchaotic system then generates pseudo-random sequences used for pixel permutation and diffusion before final AES encryption.

The approach ensures:

• Strong confidentiality and resistance to cryptographic attacks (differential, brute-force, statistical),
• High computational efficiency compared to traditional heavy encryption methods,
• Applicability to both grayscale and color images.

The literature review shows that many existing methods use chaotic maps, lightweight cryptography, or hybrid schemes, but often trade off between security, speed, or hardware efficiency. The proposed method aims to overcome these limitations by integrating multiple techniques into a layered, robust encryption architecture.

Finally, the methodology outlines steps such as feature extraction, dynamic key generation using BLAKE2b hashing, ECC-based key exchange, AES-CBC encryption, and hyperchaotic sequence generation to drive secure scrambling and diffusion of image data.

Conclusion

The effectiveness of image encryption is notably enhanced by integrating a hyperchaotic encryption technique with feature-based dynamic key generation and ECC-driven key exchange. The dynamic key approach introduces variability tied to the image content, strengthening resistance against attacks, while ECC ensures secure and efficient sharing of keys. The proposed approach incorporates a hyperchaotic mechanism for pixel permutation and diffusion, AES to protect extracted feature data, and ECC for secure key distribution. Together, these elements form a reliable, efficient, and secure framework for modern image protection, with potential extensions toward real-time video encryption and lightweight solutions for edge computing systems.

References

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Copyright

Copyright © 2026 Vasavi Sridevi Seelam, Dr. Dasari Haritha. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.