Inverse Proportional Chaotification Model for Image Encryption in IoT Scenarios

In Internet of Things (IoT) scenarios, the limited computing resources and energy constraints of devices, alongside the growing demand for real-time applications, make secure image transmission challenging. To address this issue, we propose a lightweight image encryption scheme based on chaotic map. Firstly, a new 3-D inverse proportional chaotic map (3D-IPCM) is designed with good robustness. Dynamics confirms that it possesses key characteristics, including a broad and continuous chaotic range, all positive Lyapunov exponents (LEs), high permutation entropy (PE) complexity and even distribution. Then, a new pseudorandom number generator (PRNG) is designed based on this map, which successfully passes all NIST and TestU01 tests, even with 16-bit calculation precision. Next, this PRNG is employed for image encryption in IoT scenarios. In the cryptosystem, a chromosome crossover (CC)-based scrambling algorithm is proposed, along with a diffusion algorithm that achieves strong resistance to differential attack in just two rounds of row diffusion. Simulation and analysis verify that the cryptosystem has strong resistance to common attacks and low cost. For $256 \times 256$ images, its average number of pixels change rate (NPCR) and unified average changing intensity (UACI) are 100% and 33.40%, respectively, and the encryption time is only 7.4 ms. Ultimately, we implement the algorithm on FPGA, thus confirming its capacity for parallel acceleration.