Secure Image Transmission for Industrial IoT via Dynamic Memristive Chaos and Feature-Evolutionary Diffusion

To address the trade-off between security and efficiency in Industrial IoT, this article presents a memristive chaotic system and a feature-driven encryption architecture. First, a dynamic window decay (DWD) memristor incorporating a voltage-dependent window function is proposed. By coupling this memristor with the Ikeda map, a novel chaotic model termed the DWD-Ikeda map is developed. This model exhibits high-complexity chaotic behavior and serves as the pseudorandom source. Based on this, a 3-D plaintext-feature-driven encryption scheme is developed. Then, the architecture integrates fractional-wavelet-gradient feature extraction for dynamic parameter modulation and a feature-anchored chain reset diffusion mechanism to control error propagation. Finally, performance evaluations on standard benchmarks and the NEU-DET industrial dataset show a number of pixel change rate of 99.6% and a uniform average change intensity of 33.4%. For 256 × 256 images, the software encryption time is 0.20 s, while the FPGA hardware implementation achieves a throughput of 440 Mbps. These results verify the scheme’s suitability for secure real-time industrial image transmission.