Enhanced Image Security via Dynamic Chaotic Fuzzy Cellular Neural Networks and Voice Authentication

The vulnerability of transmitted digital images has become a pressing concern due to recent advancements in multimedia technology. Conventional encryption methods often fail to meet the requirements of large-scale real-time multimedia security. In order to strengthen color image encryption, in this paper, we propose a novel encryption method that combines fuzzy cellular neural networks with dynamic audio-based biometric data, which aligns with the principle of symmetric encryption. To make the encryption process specific to each user and hard to replicate, the method uses speech characteristics—the peak frequency and zero-crossing rate—extracted from the user’s voice. By integrating these voice features into the fuzzy cellular neural network structure, the method expands the set of potential keys and enhances protection against brute-force, statistical, and chosen-plaintext attacks. Compared to conventional methods that rely solely on chaotic maps or neural networks, this approach provides a larger key space, higher entropy, and better disruption of pixel correlation. The encryption quality is validated through experimental results using the NPCR, UACI, PSNR, and SSIM metrics. Securing multimedia transmissions contributes to the broader vision of a protected society, where technological progress promotes safety, trust, and fair access to knowledge.