Cross-platform social robot detection method based on adversarial neural network
A detection method and neural network technology, applied in the field of robot detection, to achieve good versatility
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Embodiment 1
[0021] In order to solve the problem that all kinds of social robots emerge in an endless stream in social robot detection, the application scenarios of traditional algorithms are limited, see figure 1 The implementation of the present invention provides a method for detecting a cross-platform social robot based on an adversarial neural network, the method comprising the following steps:
[0022] 101: data preprocessing;
[0023] Among them, this step includes three sub-steps of data cleaning, data standardization, and attribute extraction, aiming to standardize the input format of the neural network and reduce abnormal conditions during the training process of the neural network.
[0024] 102: Account feature extraction;
[0025] First, 17 attribute values are extracted according to each account data, so as to abstractly represent an account information, and form a 1*17 one-dimensional vector, which is sent into a one-dimensional convolutional neural network, and in the vo...
Embodiment 2
[0029] The scheme in embodiment 1 is introduced in detail below in conjunction with specific calculation formulas and examples, see the following description for details:
[0030] 201: data cleaning;
[0031] Due to the data in the data set, there are some missing and incomplete attribute values and data with obvious abnormalities, which may lead to deviations in feature extraction. [8] , so it is necessary to fill in the missing values and at the same time identify outliers and discard them.
[0032] 202: data standardization;
[0033] Because the attribute values of data often have different dimensions, for example, the unit of the data attribute of the number of fans is 1, and the unit of the data attribute of the number of days of account creation is days, and the unit of the data attribute of whether to set the background of the personal homepage is Yes / No. In order to convert the data into dimensionless values, thereby getting rid of the limitation of the unit, s...
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