Secret level marking identification method based on Krawtchouk moment and KNN-SMO classifier

Abstract

The invention relates to a secret level marking identification method based on a Krawtchouk moment and a KNN-SMO classifier. The method relates to a theory based on the Krawtchouk moment and the KNN-SMO classifier applied to the secret level marking identification of the computer forensics, and the method comprises the steps: a secret level marking image is performed the image preprocessing, the feature vector is formed by calculated Krawtchouk moments of the image, and the secret level marking image is performed the classification and identification by the KNN-SMO classifier. On one side, the low-stage Krawtchouk moment can be used to express the characteristic of the image well and has good stability under the common attack, on the other side, the KNN-SMO classifier is used, so that the classifier has KNN quick sorting capacity and SMO advantage of solving small sample problem, so that precision and speed of the secret level marking identification is improved.

Description

technical field [0001] The invention belongs to the field of electronic forensics, and in particular relates to a secret-level sign recognition method based on Krawtchouk moments and a KNN-SMO classifier. Background technique [0002] In electronic forensics, after completing the retrieval of secret-related documents, it is necessary to further identify the secret-level identification of the secret-related documents, and record the results of the inspection to form a log as evidence for forensics. [0003] Existing recognition methods, common methods include template matching method (AravindaCV, PrakashHN. Template matching method for Kannada Handwritten recognition based on correlation analysis [C] / / Contemporary Computing and Informatics (IC3I), 2014International Conference on. IEEE, 2014:857-861.) [0004] , Character feature statistics method (DasS, jyotiChoudhuryS, DasAK, etal.SelectionofGraph-BasedFeaturesforCharacterRecognitionUsingSimilarityBasedFeatureDependencyandRo...

AI Technical Summary

Problems solved by technology

Aiming at the fact that the usual text recognition algorithm cannot effectively identify the classified mark after being attacked, a secret mark recognition method based on Krawtchouk moment and KNN-SMO classifier is proposed, and the characteristics of the image can be well described by using the low-order Krawtchouk moment , and the quantity has good stability under common attacks, and the use of KNN-SMO combined classifier makes the classifier not only have the ability of KNN fast classification but also has the advantage of SMO in overcoming the small sample problem, thus improving the confidentiality identification Recognition accuracy and speed

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