PREDICTION OF CORROSION RESISTANCE OF THE DEFORMED SEMI-FINISHED PRODUCTS OF STEEL 35ХГФ BASED ON DATA FROM EBSD-ANALYSIS

Коррозионная стойкость – важнейшая характеристика современных функциональных материалов. В данной работе анализируются возможности метода автоматического анализа картин дифракции обратнорассеянных электронов (EBSD) для прогнозирования коррозионной стойкости по отношению к углеводородам горячекатаных труб из стали 35ХГФ.Corrosion resistance – is the important feature of modern functional materials, in this work presented an analysis of capabilities of EBSD-method for prediction corrosion resistance steel towards hydrocarbons

Honest-but-curious nets: sensitive attributes of private inputs can be secretly coded into the classifiers' outputs

It is known that deep neural networks, trained for the classification of non-sensitive target attributes, can reveal sensitive attributes of their input data through internal representations extracted by the classifier. We take a step forward and show that deep classifiers can be trained to secretly encode a sensitive attribute of their input data into the classifier's outputs for the target attribute, at inference time. Our proposed attack works even if users have a full white-box view of the classifier, can keep all internal representations hidden, and only release the classifier's estimations for the target attribute. We introduce an information-theoretical formulation for such attacks and present efficient empirical implementations for training honest-but-curious (HBC) classifiers: classifiers that can be accurate in predicting their target attribute, but can also exploit their outputs to secretly encode a sensitive attribute. Our work highlights a vulnerability that can be exploited by malicious machine learning service providers to attack their user's privacy in several seemingly safe scenarios; such as encrypted inferences, computations at the edge, or private knowledge distillation. Experimental results on several attributes in two face-image datasets show that a semi-trusted server can train classifiers that are not only perfectly honest but also accurately curious. We conclude by showing the difficulties in distinguishing between standard and HBC classifiers, discussing challenges in defending against this vulnerability of deep classifiers, and enumerating related open directions for future studies

Effect of heat treatment conditions on electrical resistivity of 35KhGF molten steel

The authors have studied the effect of the grain structure, crystal structure and defects of 35KhGF steel samples on the character of temperature dependence of the melt specific electrical resistance at temperatures of 1450–1720 °C. Grain and crystalline structures changed as a result of heat treatment - normalization and tempering. The peculiarities of grain and crystalline structures, the defects were recognized according to the results of metallographic study. The metallographic study was carried out by diffraction of backscattered electrons-EBSD analysis. Scanning areas were chosen with the inclusion of defects in metal of technological origin, namely, microscopic discontinuities filled with gas or slag. The results of EBSD analysis are drawn as IPF-patterns; they show the texture state of the samples using the color assignment method. The microstructure of a 35KhGF steel sample after normalization at 910 °C has the smallest crystallites (of the order of 1 μm) and the largest extent of the grain boundaries. All samples have defects – discontinuities of the order of 1 μm in size. Specific electrical resistance of molten 35KhGF steel samples was measured by the method of rotating magnetic field in heating mode and subsequent cooling. For samples preliminarily normalized at 910 °C, a discrepancy in the temperature dependences of resistivity and an irreversible decrease in the resistivity temperature coefficient were observed in cooling mode of the melt. The discrepancy between the temperature dependences of the electrical resistivity and the irreversible decrease in the temperature coefficient of the resistivity was analyzed on the basis of the microinhomogeneous structure concepts of metallic melts and the pheno menon of metallurgical heredity. According to the notion of the microheterogeneous structure of metallic melts, the melting of a multiphase steel ingot does not immediately produce a homogeneous solution of the alloying elements in the iron at the atomic level, and a chemically microinhomogeneous state is maintained in a certain temperature range. Looking at the branching of the temperature dependences of the electrical resistivity, the transition of the melt into the state of true solution occurs only near the temperature T* = 1640 °C. The value of temperature T* according to the notion of the structural metallurgical heredity phenomenon depends on microstructure, phase composition and crystalline structure of the initial sample. The presence of discontinuities leads to appearance of an excess volume of melt during metal melting, which is partially retained during cooling and crystallization. In this case, the temperature coefficient of the resistivity in cooling mode is close to zero in absolute value, even at ingot cooling rates of the order of 10 °C/s the crystallization conditions change, in particular, the metal’s propensity to amorphization increases. © 2018, National University of Science and Technology MISIS. All rights reserved