Pengembangan True Random Number Generator berbasis Citra menggunakan Algoritme Kaotis

D. Risdianto, B. Prastowo
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引用次数: 0

Abstract

The security of most cryptographic systems depends on key generation using a nondeterministic RNG. PRNG generates a random numbers with repeatable patterns over a period of time and can be predicted if the initial conditions and algorithms are known. TRNG extracts entropy from physical sources to generate random numbers. However, most of these systems have relatively high cost, complexity, and difficulty levels. If the camera is directed to a random scene, the resulting random number can be assumed to be random. However, the weakness of a digital camera as a source of random numbers lies in the resulting refractive pattern. The raw data without further processing can have a fixed noise pattern. By applying digital image processing and chaotic algorithms, digital cameras can be used to generate true random numbers. In this research, for preprocessing image data used method of floyd-steinberg algorithm. To solve the problem of several consecutive black or white pixels appearing in the processed image area, the arnold-cat map algorithm is used while the XOR operation is used to combine the data and generate the true random number. NIST statistical tests, scatter and histrogram analyzes show the use of this method can produce truly random numbers
图像驱动的数字发生器开发使用了Kaotis算法
大多数加密系统的安全性依赖于使用不确定性RNG生成密钥。PRNG在一段时间内生成具有可重复模式的随机数,如果初始条件和算法已知,则可以预测。TRNG从物理源中提取熵来生成随机数。然而,这些系统中的大多数都具有相对较高的成本、复杂性和难度。如果相机被定向到一个随机的场景,产生的随机数可以被认为是随机的。然而,作为随机数来源的数码相机的弱点在于产生的折射模式。未经进一步处理的原始数据可能具有固定的噪声模式。通过应用数字图像处理和混沌算法,数码相机可以产生真正的随机数。本研究采用floyd-steinberg算法对图像数据进行预处理。为了解决处理后的图像区域出现几个连续的黑色或白色像素的问题,我们使用arnold-cat map算法,同时使用异或运算对数据进行组合生成真随机数。NIST统计测试、散点和直方图分析表明,使用这种方法可以产生真正的随机数
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