Volchikhin Vladimir Ivanovich, Doctor of engineering sciences, professor, President of Penza State University (40 Krasnaya street, Penza, Russia), firstname.lastname@example.org
Ivanov Aleksandr Ivanovich, Doctor of engineering sciences, associate professor, head of the laboratory of biometric and neural network technologies, Penza Research Institute of Electrical Engineering (9 Sovetskaya street, Penza, Russia), email@example.com
Yunin Aleksey Petrovich, Lead expert, Penza Research Institute of Electrical Engineering (9 Sovetskaya street, Penza, Russia), firstname.lastname@example.org
Malygina Elena Aleksandrovna, Candidate of engineering sciences, research worker, the interindustrial testing laboratory of biometric devices and technologies, Penza State University (40 Krasnaya street, Penza, Russia), email@example.com
Background. The aim of the work is to describe the ideal picture of “white noise” that enables to control its quality on small testing samples.
Materials and methods. Shannon entropy calculation for numerical sequences of 256 bits is a complicated problem. In this regard, the problem’s computation complexity is decreased by transition into the Hammings distances’ space, when testing neural network “biometrics-code” converters according to state standard GOST R 52633.3. In this case there occurs convolution of the problem’s dimensions. However such a computting solution may end up incorrect for small testing samples.
Results. It is suggested to use several Hamming convolutions taking place in field modulo 2, 4, 8 or higher. At the same time the volume of data on proximity of the analyzed numerical sequence’s picture to the picture of the ideal “white noise” increases. The work gives a list of defects featuring the real “white noise” observed in Hamming convolutions’ spaces and adduces the error estimates regarding features of the examined picture of “white noise” occurring due to the finite size of testing sample.
Conclusions. The procedures recommended by GOST R 52633.3 are insufficient at “deep” control of hashing properties of neural network “biometrics-code” convertersр. It is necessary to supplement the “white noise” quality control in a regular Hamming distances’ space, calculated by a code convolution modulo 2, Hamming convolutions in field with high modulo values.
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