A true random number generator based on nonlinear combined chaotic mapping for image processing
Yixuan Lv, Siwan Dong, Xinyu Zhang · 2024
In this paper, a true random number generator (TRNG) based on chaotic mapping is presented for the hardware design of image processing encryption. In order to process images more effectively, a novel nonlinear combined chaotic map (NCCM) is proposed, which makes the chaotic mapping have higher complexity and better chaotic performance, so as to improve the randomness of true random number sequence. Compared with the traditional TRNG based on function mapping, the randomness of our chaotic mapping is significantly improved. The entire system has been implemented in a standard 180 nm process under power supply of 1.2V, and the post-layout simulation results show that our work has significantly improved performance compared with traditional solutions. When our design is under the speed of 500k Bits/s, the power consumption is only 12.3μW, and the efficiency of 24.52pJ/Bit is achieved. The proposed TRNG has successfully passed the National Institute of Standards and Technology (NIST) SP800-22 random, autocorrelation and deviation test. On the basis of these test results, it is proved that proposed scheme is very suitable for the application of calibration algorithm or image processing.