Thursday 29 May 2025
The quest for secure image transmission in IoT and edge networks has led researchers to explore innovative encryption methods. A recent study proposes a novel feature-aware chaotic image encryption scheme, designed to disrupt pixel correlation and enhance security.
At its core, the scheme relies on three stages: Feature-Aware Pixel Segmentation (FAPS), Chaotic Chain Permutation, and Chaotic Chain Confusion. FAPS uses Sobel edge detection to highlight texture and edges, then classifies pixels into high-edge and low-edge regions. This preprocessing step is crucial in optimizing encryption for AI-driven IoT applications.
The second stage, Chaotic Chain Permutation, employs a logistic chaotic map to generate an initial permutation key. Each block of the image is permuted using this key, resulting in a highly randomized output. The process is repeated iteratively for all blocks, ensuring that each block’s permutation is influenced by the previous block’s hash value.
The final stage, Chaotic Chain Confusion, utilizes a similar chaotic map to generate dynamic seed matrices. These matrices are then used to perform bitwise XOR operations with each block of the image, further diffusing pixel values. The scheme’s ability to combine multiple chaotic systems and adaptively update parameters makes it highly resistant to statistical attacks.
Experimental results demonstrate that the proposed scheme achieves near-ideal entropy values and significantly reduces correlation in encrypted images. Sensitivity analysis confirms that even minor changes in the plaintext image result in substantial differences in the resulting cipher image, making it difficult for attackers to recover the original data.
This innovative approach addresses the limitations of traditional encryption methods, which often fail to provide sufficient security for resource-constrained IoT devices and large-volume image data. The proposed scheme’s lightweight and adaptive design makes it an attractive solution for real-time machine learning-based edge analytics applications.
The study’s authors have demonstrated a deep understanding of the challenges facing IoT and edge network security, and their solution offers a promising approach to addressing these concerns. As the Internet of Things continues to expand and evolve, innovative encryption methods like this one will play a critical role in ensuring the confidentiality and integrity of sensitive data.
Cite this article: “Feature-Aware Chaotic Image Encryption Scheme for Secure IoT and Edge Networks”, The Science Archive, 2025.
Image Encryption, Chaotic Systems, Iot Security, Edge Networks, Feature-Aware, Pixel Segmentation, Permutation, Confusion, Bitwise Xor, Statistical Attacks
