Poisoning attack of the training dataset for on-line signature authentication using the perceptron

Abstract

Abstract The study of the dependences of the effectiveness of a poisoning attack for the task of on-line signature authentication using a perceptron to make a decision. The dependences on the structure of the neural network, the volume of poisoning sets and the noise of the training sample were studied. Conclusions are drawn on the lack of the ability to detect such attacks by changing the overall accuracy of the system.